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Processes, Volume 8, Issue 11 (November 2020) – 196 articles

Cover Story (view full-size image): Pyrolysis of pine wood sawdust, charged/or not with activated carbon (AC), was carried out using microwave (MW) technology. Experimental conditions were consisted 20 min processing time, 0 wt.% and 10 wt.% of AC, and a MW power varying from 100 to 800 W. The results obtained showed that the MW absorber allowed increasing the bio-oil yield up to 2 folds by reducing the charcoal fraction. The higher heating values of the solid residues were ranged from 17.6 to 30.3 MJ/kg. Furthermore, the addition of AC allowed showing the probable catalytic effect of the AC in the charged sample pyrolysis bio-oils. View this paper
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21 pages, 4220 KiB  
Article
Comparative Study of Mercury(II) Removal from Aqueous Solutions onto Natural and Iron-Modified Clinoptilolite Rich Zeolite
by Marin Ugrina, Teja Čeru, Ivona Nuić and Marina Trgo
Processes 2020, 8(11), 1523; https://doi.org/10.3390/pr8111523 - 23 Nov 2020
Cited by 19 | Viewed by 2901
Abstract
The contamination of soil and water bodies with mercury from anthropogenic sources such as mining and industry activities causes negative effect for living organisms due to the process of bioaccumulation and biomagnification through the food chain. Therefore, the need for remediation of contaminated [...] Read more.
The contamination of soil and water bodies with mercury from anthropogenic sources such as mining and industry activities causes negative effect for living organisms due to the process of bioaccumulation and biomagnification through the food chain. Therefore, the need for remediation of contaminated areas is extremely necessary and very desirable when it is cost-effective by using low-cost sorbents. This paper compares the sorption abilities of natural and iron-modified zeolite towards Hg(II) ions from aqueous solutions. The influence of pH, solid/liquid ratio (S/L), contact time, and initial concentration on the sorption efficiency onto both zeolites was investigated. At the optimal pH = 2 and S/L = 10, the maximum amount of sorbed Hg(II) is 0.28 mmol/g on the natural zeolite and 0.54 mmol/g on the iron-modified zeolite. It was found that rate-controlling step in mass transfer is intraparticle diffusion accompanied by film diffusion. Ion exchange as a main mechanism, accompanied with surface complexation and co-precipitation were included in the Hg(II) sorption onto both zeolite samples. This is confirmed by the determination of the amount of sorbed Hg(II) and the amount of released exchangeable cations from the zeolite structure as well as by the scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDS) of saturated zeolite samples. In a wide pH range, 4.01 ≤ pH ≤ 11.08, the leaching of Hg(II) was observed in the amount of only 0.28–0.78% from natural zeolite and 0.07–0.51% from iron-modified zeolite indicating that both zeolites could be used for remediation purposes while the results suggest that modification significantly improves the sorption properties of zeolite. Full article
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Graphical abstract

Graphical abstract
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<p>Distribution of Hg(II) species as a function of pH.</p>
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<p>(<b>a</b>) pH<sub>e</sub> vs. pH<sub>o</sub> after sorption of Hg(II) onto NZ and FeZ. (<b>b</b>) The effect of pH<sub>o</sub> on removal efficiency, α of Hg(II) onto NZ and FeZ.</p>
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<p>The effect of solid/liquid ratio (S/L) ratio on removal efficiency, α of Hg(II) onto NZ and FeZ.</p>
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<p>Comparison of pH<sub>e</sub> with pH<sub>ppt</sub> and pH<sub>o</sub> after the sorption of Hg(II) onto NZ and FeZ at different S/L ratios.</p>
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<p>The amount of Hg(II) sorbed on NZ and FeZ as well as the removal efficiency in relation to contact time.</p>
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<p>(<b>a</b>) Fitting of experimental data with Pseudo-first-order model (PFO), Pseudo-second-order (PSO), Vermeulen’s approximation, double-exponential model (DEM), and the Bangham model. (<b>b</b>) Fitting of experimental data with the Weber–Morris model.</p>
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<p>(<b>a</b>) The amount of Hg(II) sorbed per gram of NZ and FeZ <span class="html-italic">vs</span>. c<sub>o</sub> and (<b>b</b>) the removal efficiency of Hg(II) vs. c<sub>o</sub>.</p>
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<p>The relation between sorbed Hg(II) ions and released exchangeable cations and pH<sub>e</sub> in function of initial Hg(II) concentration.</p>
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<p>Backscattered electrons mode (BSE) images of NZHg and FeZHg with marked surfaces (Spectra, Sp) for energy dispersive X-ray (EDS) analysis.</p>
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<p>SEM secondary electron image (<b>left</b>) and corresponding backscattered electron image with three marked points for saturated NZHg (<b>right</b>).</p>
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<p>SEM secondary electron image (<b>left</b>) and corresponding backscattered electron image with three marked points for saturated FeZHg (<b>right</b>).</p>
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<p>Amount of leached Hg(II) from saturated NZHg and FeZHg as a function of pH<sub>o</sub> as well as pH<sub>e</sub> as a function of pH<sub>o</sub>.</p>
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22 pages, 10836 KiB  
Article
Theoretical Evaluation of the Melting Efficiency for the Single-Screw Micro-Extrusion Process: The Case of 3D Printing of ABS
by Andrea La Gala, Rudinei Fiorio, Mustafa Erkoç, Ludwig Cardon and Dagmar R. D’hooge
Processes 2020, 8(11), 1522; https://doi.org/10.3390/pr8111522 - 23 Nov 2020
Cited by 31 | Viewed by 5035
Abstract
One of the challenges for single-screw micro-extrusion or additive manufacturing (AM), thus 3D printing, of polymers is controlling the melting efficiency so that energy and equipment costs can be minimized. Here, a numerical model is presented for AM process design, selecting acrylonitrile–butadiene–styrene (ABS) [...] Read more.
One of the challenges for single-screw micro-extrusion or additive manufacturing (AM), thus 3D printing, of polymers is controlling the melting efficiency so that energy and equipment costs can be minimized. Here, a numerical model is presented for AM process design, selecting acrylonitrile–butadiene–styrene (ABS) as viscoelastic reference polymer. It is demonstrated that AM melting is different compared to conventional melting due to variation in extrusion dimensions, leading to a different balance in heating by conduction and viscous heat dissipation as caused by the shearing between the melt layers in the associated film layer near the barrel. The thickness of this melt film layer is variable along the screw length, and it is shown that simplified models assuming an overall average value are too approximate. It is highlighted that the screw frequency, pitch angle and compression ratio are important process parameters to control the point of melt finalization. In addition, the power-law index reflecting the shear thinning nature of the polymer melt is showcased as a key parameter. Moreover, AM process results assuming constant and temperature dependent specific heat capacities and thermal conductivities are compared. The current work opens the door for on-line AM process control, addressing all relevant operating and material parameters. Full article
(This article belongs to the Special Issue Tailoring Polymeric Materials for Specific Applications)
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Figure 1
<p>Definitions of x and z directions for the general equations for melt removal by drag.</p>
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<p>Polymer melting in the consecutive cross-sections of a micro-extruder; settings in <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2). The right-hand side illustrates the concept of melt removal by drag. Blue, solid; red, molten. Melting is done once the solid bed width (<span class="html-italic">X</span>) vanishes.</p>
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<p>(<b>a</b>) Variation of velocities in compression and metering section for micro-extrusion (ME) case (blue full line, material properties, dimensions and settings from <a href="#processes-08-01522-t001" class="html-table">Table 1</a>, Column 2 and conventional/standard extrusion (SE) case with same screw speed and barrel temperature (red dashed line, material properties, dimensions and settings from <a href="#processes-08-01522-t001" class="html-table">Table 1</a>, Column 3; (<b>b</b>) corresponding variation of shear rates that are in the compression section defined based on the relative velocity and the melt layer thickness and in the metering section (strictly as soon as the melting is done) based on the total channel height and the drag flow definition; (<b>c</b>) the associated variation of viscosity; and (<b>d</b>) the associated variation of the Brinkman number.</p>
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<p>Extra information for <a href="#processes-08-01522-f003" class="html-fig">Figure 3</a> regarding: (<b>a</b>) melting efficiency parameter <span class="html-italic">Φ</span>; (<b>b</b>) melt film layer thickness <span class="html-italic">δ</span>; and (<b>c</b>) solid bed profile. In (<b>b</b>), the overall average melt layer thickness (<math display="inline"><semantics> <mover accent="true"> <mi>δ</mi> <mo stretchy="false">¯</mo> </mover> </semantics></math>) is also provided as horizontal lines.</p>
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<p>Relevance of model with explicit melt layer thickness variation versus an average one (either evaluation at initial value (orange dashed line) or actual overall average of detailed simulation (brown dashed line)): (<b>a</b>) solid bed profile; and (<b>b</b>) Brinkman number. The case of micro-extruder in <a href="#processes-08-01522-f003" class="html-fig">Figure 3</a> and <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Variation of velocities in compression and metering section for micro-extrusion (ME) case (blue full line, material properties, dimensions and settings from <a href="#processes-08-01522-t001" class="html-table">Table 1</a>, Column 2 and conventional/standard extrusion (SE) case with same screw speed and barrel temperature (red dashed line, material properties, dimensions and settings from <a href="#processes-08-01522-t001" class="html-table">Table 1</a>, Column 4; (<b>b</b>) corresponding variation of shear rates that are in the compression section defined based on the relative velocity and the melt layer thickness and in the metering section (strictly as soon as the melting is done) based on the total channel height and the drag flow definition; (<b>c</b>) the associated variation of viscosity; and (<b>d</b>) the associated variation of the Brinkman number.</p>
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<p>(<b>a</b>) Melting profile for different barrel temperatures considering otherwise the micro-extrusion settings and material parameters of <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2); and (<b>b</b>) associated Brinkman numbers. Blue (reference) lines as in <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Melting profile; and (<b>b</b>) Brinkman number for different screw frequencies under flood feeding considering otherwise the micro-extrusion settings and material parameters of <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2). Blue (reference) lines as in <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Melting profile; and (<b>b</b>) Brinkman number for different compression ratios (C.R. values) considering otherwise the micro-extrusion settings and material parameters of <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2). Blue (reference) lines as in <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Melting profile; (<b>b</b>) variation of Brinkman number; (<b>c</b>) velocity in the x direction <span class="html-italic">V<sub>bx</sub></span>; (<b>d</b>) melt layer thickness <span class="html-italic">δ</span>; and (<b>e</b>) viscosity for different pitch angles considering otherwise the micro-extrusion settings and material parameters of <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2). Blue (reference) lines as in <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Melting profile; and (<b>b</b>) Brinkman number for different polymer solid densities considering otherwise the micro-extrusion settings and material parameters of <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2). Blue (reference) lines as in <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>(<b>a</b>) Melting profile; (<b>b</b>) Brinkman number; and (<b>c</b>) melt layer thickness <span class="html-italic">δ</span> for different specific heat capacities (both solid and melt) considering otherwise the micro-extrusion settings and material parameters of <a href="#processes-08-01522-t001" class="html-table">Table 1</a> (Column 2). Blue (reference) lines as in <a href="#processes-08-01522-f004" class="html-fig">Figure 4</a>.</p>
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<p>Variations of (<b>a</b>) the thermal conductivity and (<b>b</b>) specific heat capacities due to the temperature dependencies in (<b>c</b>), also displaying in blue the micro-extrusion reference case results in <a href="#processes-08-01522-t001" class="html-table">Table 1</a> with constant thermochemical properties (423 K always).</p>
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<p>For the micro-extrusion settings in <a href="#processes-08-01522-f013" class="html-fig">Figure 13</a>: (<b>a</b>) melt profile; (<b>b</b>) the variation of the Brinkman number; (<b>c</b>) the melt layer thickness; and (<b>d</b>) the viscosity. Blue lines are for the reference case in <a href="#processes-08-01522-t001" class="html-table">Table 1</a> with constant physicochemical properties. T.D., temperature dependent.</p>
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13 pages, 4736 KiB  
Article
Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD
by Donggeun Park and Jeung Sang Go
Processes 2020, 8(11), 1521; https://doi.org/10.3390/pr8111521 - 23 Nov 2020
Cited by 18 | Viewed by 20297
Abstract
In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional [...] Read more.
In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design. Full article
(This article belongs to the Special Issue Applied Computational Fluid Dynamics (CFD))
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Figure 1
<p>Geometry of the cyclone separator.</p>
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<p>Research flow chart.</p>
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<p>Computational domain of cyclone separators.</p>
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<p>Design space for dataset; x1, x2, x3, and x4 is design variable of cyclone.</p>
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<p>Geometric schematic diagram.</p>
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<p>Comparison results of the velocity distribution experiment and the prediction results according to the turbulence model.</p>
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<p>The force analysis acting on particle; (<b>a</b>) 1 μm behavior, (<b>b</b>) 1.5 μm behavior, (<b>c</b>) 5 μm behavior.</p>
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<p>The averaged force results acting during the separation time with the separation efficiency curve.</p>
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<p>Hyperparameter tuning results.</p>
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<p>The result of comparing the prediction performance of the MLR model and the neural network model.</p>
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<p>The training results and prediction results by the NN and MLR; (<b>a</b>) Neural network results; (<b>b</b>) Multi linear regression.</p>
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27 pages, 16998 KiB  
Article
First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes
by Tiago J. Rato, Pedro Delgado, Cristina Martins and Marco S. Reis
Processes 2020, 8(11), 1520; https://doi.org/10.3390/pr8111520 - 23 Nov 2020
Cited by 10 | Viewed by 2953
Abstract
Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to [...] Read more.
Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well represented in this reference dataset (using the terminology adopted by the founding father of process monitoring, Walter A. Shewhart). Otherwise, false alarms will inevitably occur during the implementation of the monitoring scheme. However, we argue and demonstrate in this article, that this assumption is often not met in modern industrial systems. Therefore, we introduce a new approach based on the rigorous mechanistic modeling of the dominant modes of common cause variation and the use of stochastic computational simulations to enrich the historical dataset with augmented data representing a comprehensive coverage of the actual operational space. We show how to compute the monitoring statistics and set their control limits, as well as to conduct fault diagnosis when an abnormal event is declared. The proposed method, called AGV (Artificial Generation of common cause Variability) is applied to a Surface Mount Technology (SMT) production line of Bosch Car Multimedia, where more than 17 thousand product variables are simultaneously monitored. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Figure 1
<p>Projection of data from several production periods (designated as CS1, CS2 and CS3) on the principal components space estimated using CS1 as the reference dataset: (<b>a</b>) Scores plot of PC1 vs. PC2; (<b>b</b>) Scores plot of PC1 vs. PC3. The 99% confidence ellipses for the scores of CS1 are also represented; (<b>c</b>) MSPM-PCA monitoring statistics (Hotelling’s <span class="html-italic">T<sup>2</sup></span> of the scores and the <span class="html-italic">Q</span> or <span class="html-italic">SPE</span> statistic of the residuals; see <a href="#sec4-processes-08-01520" class="html-sec">Section 4</a>) for CS1, using CS1 as the reference dataset; (<b>d</b>) MSPM-PCA monitoring statistics for CS3, using CS1 as the reference dataset.</p>
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<p>Schematic representation of the AGV module.</p>
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<p>Schematic representation of the inter-lot, intra-lot and pad specific components used to generate the translation effect on offset-X.</p>
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<p>Schematic representation of the inter-lot and intra-lot components used to generate the rotation effect on offset-X and offset-Y. The cross represents the specific center of rotation of each PCB.</p>
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<p>Schematic representation of inter-lot, intra-lot and pad specific components used to generate the solder mask effect on height.</p>
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<p>Schematic representation of the squeegee effect on height. Dimensions are not to scale.</p>
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<p>Scree plot of the first 20 eigenvalues of the PCA model.</p>
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<p>Loadings of the first six principal components. Each block of loadings corresponds to (from left to right) areas, heights, volumes, offset-X and offset-Y.</p>
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<p>Relation between the principal components of the simulated validation lots and the data <a href="#processes-08-01520-f001" class="html-fig">Figure 1</a>. (<b>a</b>) first and second principal components; (<b>b</b>) first and fourth principal components; (<b>c</b>) third and fourth principal components; (<b>d</b>) fourth and fifth principal components.</p>
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<p>Control charts for the PCBs in CS1: (<b>a</b>) <span class="html-italic">T<sup>2</sup></span>-statisctic; (<b>b</b>) <span class="html-italic">Q</span>-statistic.</p>
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<p>Principal components of CS2: (<b>a</b>) first principal component; (<b>b</b>) fourth principal component.</p>
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<p>Relation between the principal components of the simulated validation lots and the data from CS2: (<b>a</b>) first and second principal components; (<b>b</b>) first and fourth principal components; (<b>c</b>) third and fourth principal components; (<b>d</b>) fourth and fifth principal components.</p>
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<p>Second and fifth principal components of CS2 stratified by printing direction: (<b>a</b>) PCBs #435 to #834. (<b>b</b>) PCBs #835 to #1595.</p>
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<p>Control charts for the PCBs in CS2: (<b>a</b>) <span class="html-italic">T<sup>2</sup></span>-statisctic; (<b>b</b>) <span class="html-italic">Q</span>-statistic.</p>
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<p>Contribution of each pads’ parameter to the <span class="html-italic">Q</span>-statistic of PCB #159 of CS2: (<b>a</b>) contributions for the pads’ area; (<b>b</b>) contributions for the pads’ height.</p>
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<p>Principal components of CS3: (<b>a</b>) first principal component; (<b>b</b>) fourth principal component.</p>
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<p>Relation between the principal components of the simulated validation lots and the data from CS3: (<b>a</b>) first and second principal components; (<b>b</b>) first and fourth principal components; (<b>c</b>) third and fourth principal components; (<b>d</b>) fourth and fifth principal components.</p>
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<p>Relation between the principal components of the simulated validation lots and the data from CS3: (<b>a</b>) first and second principal components; (<b>b</b>) first and fourth principal components; (<b>c</b>) third and fourth principal components; (<b>d</b>) fourth and fifth principal components.</p>
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<p>Control charts for the PCBs in CS3: (<b>a</b>) <span class="html-italic">T<sup>2</sup></span>-statisctic; (<b>b</b>) <span class="html-italic">Q</span>-statistic.</p>
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<p>Contribution of the pads’ height to the <span class="html-italic">Q</span>-statistic of PCB #248 of CS3.</p>
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14 pages, 1954 KiB  
Article
Evaluation for the Removal Efficiency of VOCs and Heavy Metals by Zeolites-Based Materials in the Wastewater: A Case Study in the Tito Scalo Industrial Area
by Maura Mancinelli, Antonella Arfè, Annalisa Martucci, Luisa Pasti, Tatiana Chenet, Elena Sarti, Giulia Vergine and Claudia Belviso
Processes 2020, 8(11), 1519; https://doi.org/10.3390/pr8111519 - 22 Nov 2020
Cited by 11 | Viewed by 3189
Abstract
The current study was designed to demonstrate the efficiency of selected zeolites in the immobilization of heavy metals and volatile organic compounds from water in the industrial area of Tito Scalo (Basilicata Region in Southern Italy). The efficiency of zeolite materials has been [...] Read more.
The current study was designed to demonstrate the efficiency of selected zeolites in the immobilization of heavy metals and volatile organic compounds from water in the industrial area of Tito Scalo (Basilicata Region in Southern Italy). The efficiency of zeolite materials has been evaluated by analyzing real water samples, by a multi-technique approach. Gas chromatography (GC) and inductively coupled plasma optical emission spectrometry (ICP-OES) were selected for the detection of volatile organic compounds (VOCs) and heavy metals respectively, and then by thermal analysis (TG, DTA) and X-ray powder diffraction (XRD) to verify the presence of contaminants in the structural channels of the adsorbents. ZSM-5 zeolite (MFI topology) was suitable for volatile organic compounds, showing removal efficiencies 87%. 13X (FAU topology) was more selective for in situ abatements of heavy metals, with efficiencies up to 100%. After VOCs and heavy metals removal, structure refinements of loaded zeolites highlighted variations of both lattice parameters and extraframework content confirming the pollutants immobilization in the framework microporosities. The occurrence of these species was also confirmed by DTA curves showing different phenomena explained on the basis of the nature and number of extraframework species hosted in the zeolite micropores. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>View of the study area with the sampling points and geographic coordinates.</p>
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<p>X-ray powders diffraction patterns of 13X (<b>a</b>) and ZSM-5 (<b>b</b>) zeolites after adsorption of water from different areas inside the SIN.</p>
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<p>FWHM values as a function of 2theta (°) of ZSM-5 (<b>a</b>) and 13X (<b>b</b>).</p>
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<p>DTA curves of 13X (<b>left</b>) and ZSM-5 (<b>right</b>) zeolites after adsorption of water from different areas inside the SIN.</p>
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22 pages, 4370 KiB  
Article
Production of Protein Hydrolysate Containing Antioxidant and Angiotensin -I-Converting Enzyme (ACE) Inhibitory Activities from Tuna (Katsuwonus pelamis) Blood
by Natthaphon Mongkonkamthorn, Yuwares Malila, Suthasinee Yarnpakdee, Sakunkhun Makkhun, Joe M. Regenstein and Sutee Wangtueai
Processes 2020, 8(11), 1518; https://doi.org/10.3390/pr8111518 - 22 Nov 2020
Cited by 27 | Viewed by 3849
Abstract
Tuna blood (TB) was subjected to enzymatic hydrolysis. The effects of the relationship of hydrolysis time (30–180 min) and enzyme concentration (0.5–3.0% w/w protein) on the degree of hydrolysis (DH), yield, antioxidant and angiotensin-I-converting enzyme (ACE) inhibitory activities were determined. The response surface [...] Read more.
Tuna blood (TB) was subjected to enzymatic hydrolysis. The effects of the relationship of hydrolysis time (30–180 min) and enzyme concentration (0.5–3.0% w/w protein) on the degree of hydrolysis (DH), yield, antioxidant and angiotensin-I-converting enzyme (ACE) inhibitory activities were determined. The response surface methodology (RSM) showed that TB hydrolysis’s optimum conditions were hydrolysis for 180 min and Alcalase, Neutrase or Flavourzyme at 2.81%, 2.89% or 2.87% w/w protein, respectively. The hydrolysates with good DH (40–46%), yield (3.5–4.6%), the IC50 of DPPH (0.8–1.6 mg/mL) and ABTS (1.0–1.4 mg/mL) radical scavenging activity, ferric reducing antioxidant power (FRAP) value (0.28–0.65 mmol FeSO4/g) and IC50 of ACE inhibitory activity (0.15–0.28 mg/mL) were obtained with those conditions. The TB hydrolysate using Neutrase (TBHN) was selected for characterization in terms of amino acid composition, peptide fractions and sensory properties. The essential, hydrophobic and hydrophilic amino acids in TBHN were ~40%, 60% and 20% of total amino acids, respectively. The fraction of molecular weight <1 kDa showed the highest antioxidant and ACE inhibitory activities. Fishiness and bitterness were the main sensory properties of TBHN. Fortification of TBHN in mango jelly at ≤ 0.5% (w/w) was accepted by consumers as like moderately to like slightly, while mango jelly showed strong antioxidant and ACE inhibitory activities. TBHN could be developed for natural antioxidants and antihypertensive peptides in food and functional products. Full article
(This article belongs to the Section Food Process Engineering)
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<p>Response surface plots for the effect of hydrolysis time (X<sub>1</sub>; min) and enzyme concentration (X<sub>2</sub>; %) on the DH, yield, DPPH radical scavenging activity, ABTS radical scavenging activity, FRAP assay and ACE inhibitory activity using Alcalase (<b>A1</b>–<b>F1</b>), Neutrase (<b>A2</b>–<b>F2</b>) and Flavourzyme (<b>A3</b>–<b>F3</b>) hydrolysis.</p>
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<p>Response surface plots for the effect of hydrolysis time (X<sub>1</sub>; min) and enzyme concentration (X<sub>2</sub>; %) on the DH, yield, DPPH radical scavenging activity, ABTS radical scavenging activity, FRAP assay and ACE inhibitory activity using Alcalase (<b>A1</b>–<b>F1</b>), Neutrase (<b>A2</b>–<b>F2</b>) and Flavourzyme (<b>A3</b>–<b>F3</b>) hydrolysis.</p>
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<p>Response surface plots for the effect of hydrolysis time (X<sub>1</sub>; min) and enzyme concentration (X<sub>2</sub>; %) on the DH, yield, DPPH radical scavenging activity, ABTS radical scavenging activity, FRAP assay and ACE inhibitory activity using Alcalase (<b>A1</b>–<b>F1</b>), Neutrase (<b>A2</b>–<b>F2</b>) and Flavourzyme (<b>A3</b>–<b>F3</b>) hydrolysis.</p>
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<p>Main effect plot of hydrolysis time (X<sub>1</sub>) and enzyme concentration (X<sub>2</sub>) for DH, yield, DPPH radical scavenging activity, ABTS radical scavenging activity, FRAP assay and ACE inhibitory activity of TBH using Alcalase (<b>A</b>), Neutrae (<b>B</b>) and Flavourzyme (<b>C</b>).</p>
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<p>Interaction plot of hydrolysis time (X<sub>1</sub>) and enzyme concentration (X<sub>2</sub>) for DH, yield, DPPH radical scavenging activity, ABTS radical scavenging activity, FRAP assay and ACE inhibitory activity of TBH using Alcalase (<b>A</b>), Neutrae (<b>B</b>) and Flavourzyme (<b>C</b>).</p>
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<p>Sensory acceptability score of mango jelly product with and without TB hydrolysate. Different lowercase letters for the same attributes indicate significant differences (<span class="html-italic">p</span> ≤ 0.05).</p>
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17 pages, 4346 KiB  
Article
Numerical Investigation of the Characteristics of the In-Cylinder Air Flow in a Compression-Ignition Engine for the Application of Emulsified Biofuels
by Mohd Fadzli Hamid, Mohamad Yusof Idroas, Mazlan Mohamed, Shukriwani Sa'ad, Teoh Yew Heng, Sharzali Che Mat, Muhamad Azman Miskam and Muhammad Khalil Abdullah
Processes 2020, 8(11), 1517; https://doi.org/10.3390/pr8111517 - 22 Nov 2020
Cited by 4 | Viewed by 3277
Abstract
This paper presents a numerical analysis of the application of emulsified biofuel (EB) to diesel engines. The study performs a numerical study of three different guide vane designs (GVD) that are incorporated with a shallow depth re-entrance combustion chamber (SCC) piston. The GVD [...] Read more.
This paper presents a numerical analysis of the application of emulsified biofuel (EB) to diesel engines. The study performs a numerical study of three different guide vane designs (GVD) that are incorporated with a shallow depth re-entrance combustion chamber (SCC) piston. The GVD variables were used in three GVD models with different vane heights, that is, 0.2, 0.4 and 0.6 times the radius of the intake runner (R) and these were named 0.20R, 0.40R and 0.60R. The SCC piston and GVD model were designed using SolidWorks 2017, while ANSYS Fluent version 15 was used to perform cold flow engine 3D analysis. The results of the numerical study showed that 0.60R is the optimum guide vane height, as the turbulence kinetic energy (TKE), swirl ratio (Rs), tumble ratio (RT) and cross tumble ratio (RCT) in the fuel injection region improved from the crank angle before the start of injection (SOI) and start of combustion (SOC). This is essential to break up the heavier-fuel molecules of EB so that they mix with the surrounding air, which eventually improves the engine performance. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>YANMAR L70AE CI generator diesel engine.</p>
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<p>Schematic diagram of the engine setup.</p>
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<p>Schematic diagram for geometry design of the shallow depth re-entrance combustion chamber (SCC) piston bowl (unit in mm).</p>
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<p>Installation of guide vane design (GVD) at the intake manifold.</p>
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<p>Design of GVD.</p>
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<p>Swirl, tumble, and cross tumble ratio orientations.</p>
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<p>In-cylinder pressure against crank angle (θ).</p>
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<p>In-cylinder turbulence kinetic energy (TKE) against crank angle (θ).</p>
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<p>Swirl ratio (Rs) against the crank angle (θ).</p>
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<p>Tumble ratio (R<sub>T</sub>) against the crank angle (θ).</p>
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<p>Cross tumble ratio (R<sub>CT</sub>) against the crank angle (θ).</p>
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<p>The computed streamline intake stroke crank angle at 30° (<b>top</b>), 60° (<b>middle</b>) and 90° (<b>bottom</b>) showing the swirl and tumble structure.</p>
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<p>The computed streamline compression stroke at the 346° (<b>top</b>), 330° (<b>middle</b>) and 310° (<b>bottom</b>) crank angle.</p>
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14 pages, 3093 KiB  
Article
Hot Melt Extrusion Processing Parameters Optimization
by Abdullah Alshetaili, Saad M. Alshahrani, Bjad K. Almutairy and Michael A. Repka
Processes 2020, 8(11), 1516; https://doi.org/10.3390/pr8111516 - 22 Nov 2020
Cited by 24 | Viewed by 5927
Abstract
The aim of this study was to demonstrate the impact of processing parameters of the hot-melt extrusion (HME) on the pharmaceutical formulation properties. Carbamazepine (CBZ) was selected as a model water-insoluble drug. It was incorporated into Soluplus®, which was used as [...] Read more.
The aim of this study was to demonstrate the impact of processing parameters of the hot-melt extrusion (HME) on the pharmaceutical formulation properties. Carbamazepine (CBZ) was selected as a model water-insoluble drug. It was incorporated into Soluplus®, which was used as the polymeric carrier, to produce a solid dispersion model system. The following HME-independent parameters were investigated at different levels: extrusion temperature, screw speed and screw configuration. Design of experiment (DOE) concept was applied to find the most significant factor with minimum numbers of experimental runs. A full two-level factorial design was applied to assess the main effects, parameter interactions and total error. The extrudates’ CBZ content and the in vitro dissolution rate were selected as response variables. Material properties, including melting point, glass transition, and thermal stability, and polymorphs changes were used to set the processing range. In addition, the extruder torque and pressure were used to find the simplest DOE model. Each change of the parameter showed a unique pattern of dissolution profile, indicating that processing parameters have an influence on formulation properties. A simple, novel and two-level factorial design was able to evaluate each parameter effect and find the optimized formulation. Screw configuration and extrusion temperature were the most affecting parameters in this study. Full article
(This article belongs to the Special Issue Pharmaceutical Development and Bioavailability Analysis)
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<p>Thermo-gravimetric analysis (TGA) data for pre-extrusion for physical mixture and pure carbamazepine (CBZ).</p>
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<p>Differential scanning calorimetry (DSC) thermograms for pure CBZ and the pure Soluplus<sup>®</sup>.</p>
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<p>Schematic diagram of the screw configurations (generated with twin-screw configurator, version: 1.30 (8)).</p>
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<p>DSC data for pure CBZ and different formulations (F1–F8) (15:85; CBZ: Soluplus<sup>®</sup>).</p>
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<p>X-ray diffraction (XRD) data for pure CBZ and different formulations (F1–F8) (15:85; CBZ: Soluplus<sup>®</sup>).</p>
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<p>In vitro release profiles of pure CBZ and different formulations (F1–F8) (15:85; CBZ: Soluplus<sup>®</sup>).</p>
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<p>Diagnostics plots for 2-level factorial design, including normal plots of residuals, residual vs. run, and predicted vs. actual plots.</p>
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<p>Design of space of a 2-level factorial design without a center point, for an optimum formulation.</p>
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12 pages, 1597 KiB  
Article
Nitrogen Recovery from Swine Manure Using a Zeolite-Based Process
by Markus Ellersdorfer, Sandro Pesendorfer and Kristina Stocker
Processes 2020, 8(11), 1515; https://doi.org/10.3390/pr8111515 - 21 Nov 2020
Cited by 10 | Viewed by 3016
Abstract
Intensive pig farming produces huge amounts of swine manure, which can cause regional nutrient imbalances and represent a potential source of soil and water pollution due to manure over-application. In order to improve nutrient stewardship, technologies for manure treatment and selective recovery of [...] Read more.
Intensive pig farming produces huge amounts of swine manure, which can cause regional nutrient imbalances and represent a potential source of soil and water pollution due to manure over-application. In order to improve nutrient stewardship, technologies for manure treatment and selective recovery of nutrients (especially ammonia) have to be developed to foster agriculture–food system sustainability. In the present study, a combined stripping and ion exchange process using natural zeolite (ion-exchanger-loop-stripping process) is tested for nitrogen recovery from swine manure to determine its technical feasibility in this novel field of application. Ammonium removal rates of 85 to 96% were achieved in pilot scale experiments with preprocessed manure (~500 L h−1 after mechanical filtration; input concentration: ~1.3 g NH4+ L−1). NH4+ was successfully transferred to a concentrated ammonium sulfate solution (final concentration: 66 g NH4+ L−1), with no significant transfer of other manure components. Hence, various utilizations of the product solution are possible, e.g., for industrial off-gas cleaning (DeNOx) or as raw material for fertilizer production. Based on these findings, the ILS-process can be regarded as a promising option for nitrogen recovery from swine manure. Full article
(This article belongs to the Special Issue Sustainable Remediation Processes Based on Zeolites)
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<p>Illustration of the sample extraction from the mixed and breeding manure tanks.</p>
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<p>Principle flowchart of the ILS-pilot plant (treatment capacity: 500–1000 L h<sup>−1</sup>) and the different operation modes of the ion exchanger columns (single/serial).</p>
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<p>Ammonium removal rates (%) and input concentrations (mg NH<sub>4</sub><sup>+</sup> L<sup>−1</sup>) of different manure qualities after preprocessing (single column operation).</p>
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<p>Ammonium removal rates (%) from and input concentrations c<sub>0</sub> after different operation times for single (K1) and serial column operation (K1 + K2) using flocked breeding manure.</p>
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16 pages, 2799 KiB  
Article
Quantifying the Effect of COD to TN Ratio, DO Concentration and Temperature on Filamentous Microorganisms’ Population and Trans-Membrane Pressure (TMP) in Membrane Bio-Reactors (MBR)
by Petros Gkotsis, Giannis Lemonidis, Manassis Mitrakas, Alexandros Pentedimos, Margaritis Kostoglou and Anastasios Zouboulis
Processes 2020, 8(11), 1514; https://doi.org/10.3390/pr8111514 - 21 Nov 2020
Cited by 8 | Viewed by 2662
Abstract
Using moderate populations of filaments in the biomass of Membrane Bio-Reactors (MBRs) is a biological anti-fouling method which has been increasingly applied over the last few years. This study aims to quantify the effect of COD to TN ratio, Dissolved Oxygen (DO) concentration [...] Read more.
Using moderate populations of filaments in the biomass of Membrane Bio-Reactors (MBRs) is a biological anti-fouling method which has been increasingly applied over the last few years. This study aims to quantify the effect of COD to TN ratio, Dissolved Oxygen (DO) concentration and temperature on filaments’ population and Trans-Membrane Pressure (TMP) in a pilot-scale MBR, with a view to reducing membrane fouling. The novelty of the present work concerns the development of a mathematical equation that correlates fouling rate (dTMP/dt) with the population of filamentous microorganisms, assessed by the Filament Index (FI), and with the concentration of the carbohydrate fraction of Soluble Microbial Products (SMPc). Apart from TMP and SMPc, other fouling-related biomass characteristics, such as sludge filterability and settleability, were also examined. It was shown that at high COD to TN ratio (10:1), low DO concentration in the filaments’ tank (0.5 ± 0.3 mg/L) and high temperature (24–30 °C), a moderate population of filaments is developed (FI = 1–2), which delays the TMP rise. Under these conditions, sludge filterability and settleability were also enhanced. Finally, TMP data analysis showed that the fouling rate is affected by FI and SMPc concentration mainly in the long-term fouling stage and increases exponentially with their increase. Full article
(This article belongs to the Special Issue Wastewater Treatment Processes)
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<p>Schematic of pilot-scale MBR setup.</p>
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<p>Evolution of Trans-Membrane Pressure (TMP) during filtration under constant flow rate conditions in MBRs.</p>
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<p>Typical optical microscopy images from: (<b>a</b>) Phase A (FI = 0–1, T = 25–30 °C); (<b>b</b>) Phase B (FI = 1–2, T= 24–30 °C); (<b>c</b>) Phase C (FI = 2–3, T = 18–22 °C); (<b>d</b>) Phase D (FI = 3–4, T = 15–19 °C).</p>
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<p>Effect of Filament Index (FI) and temperature on TMP during: (<b>a</b>) Phase A; (<b>b</b>) Phase B; (<b>c</b>) Phase C; (<b>d</b>) Phase D.</p>
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<p>Effect of FI and temperature on SMP<sub>c</sub> concentration.</p>
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<p>Effect of FI and temperature on sludge filterability (TTF).</p>
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<p>Effect of FI and temperature on sludge settleability (SVI).</p>
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<p>Experimental and theoretical TMP evolution during: (<b>a</b>) Phase A; (<b>b</b>) Phase B; (<b>c</b>) Phase C; (<b>d</b>) Phase D.</p>
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<p>Correlation of dTMP/dt with FI and SMP<sub>c</sub> concentration for: (<b>a</b>) <span class="html-italic">Stage 2</span> (long-term fouling) and (<b>b</b>) <span class="html-italic">Stage 3</span> (TMP jump).</p>
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15 pages, 1636 KiB  
Article
Dog Rabies in Dhaka, Bangladesh, and Implications for Control
by Masud M A, Md Hamidul Islam, Muhaiminul Islam Adnan and Chunyoung Oh
Processes 2020, 8(11), 1513; https://doi.org/10.3390/pr8111513 - 21 Nov 2020
Cited by 5 | Viewed by 3940
Abstract
Controlling rabies among free-roaming street dogs has been a huge challenge in many parts of the world. Vaccination is a commonly used strategy to control rabies, however, sufficient vaccination coverage is very challenging when it comes to street dogs. Also, dog rabies data [...] Read more.
Controlling rabies among free-roaming street dogs has been a huge challenge in many parts of the world. Vaccination is a commonly used strategy to control rabies, however, sufficient vaccination coverage is very challenging when it comes to street dogs. Also, dog rabies data is scarce, making it difficult to develop proper strategies. In this study, we use a logistic growth incorporated epidemic model to understand the prevalence of rabies in the dog population of Dhaka, Bangladesh. The study shows that, the basic reproduction number for dog rabies in Dhaka lies between 1.1 to 1.249 and the environmental carrying capacity lies approximately between 58,110 to 194,739. Considering the vaccination and neuter programs administered in the last decade, we attempt to explain rabies transmission among dogs in this population. We found that the high basic reproduction number is associated with high environmental carrying capacity and vice versa. Further, we compare different type of control strategies, viz., constant vaccination, pulse vaccination, and optimal vaccination strategies. In the case of high environmental carrying capacity, vaccination, and neuter strategy is not sufficient for controlling rabies in street dogs, whereas carrying capacity control through waste management coupled with vaccination and neuter is more effective. Full article
(This article belongs to the Special Issue Development of In Vitro Disease Modelling)
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<p>(<math display="inline"><semantics> <mrow> <mi>β</mi> <mo>,</mo> <mi>γ</mi> </mrow> </semantics></math>)-parameter plane for <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. Below the solid orange curve (region I), the system remains disease-free, whereas, rabies persists beyond this curve. Region III represents the ranges for <math display="inline"><semantics> <mi>β</mi> </semantics></math> and <math display="inline"><semantics> <mi>γ</mi> </semantics></math>, which lead to periodic oscillatory solutions. The population becomes extinct in region IV. The figure on the right shows the trajectories corresponding to the four different regions of the bifurcation diagram.</p>
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<p>The curve shows the <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>D</mi> </mrow> </semantics></math> pairs that explain dog population growth between the years 2013 and 2016 in Dhaka city, where <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math> is the basic reproduction number and <span class="html-italic">D</span> is the environmental carrying capacity of dogs.</p>
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<p>Different types of control strategies are compared for (<b>A</b>) vaccination coverage, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and basic reproduction number <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.23</mn> </mrow> </semantics></math>, (<b>B</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.1</mn> </mrow> </semantics></math>, (<b>C</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.23</mn> </mrow> </semantics></math>, and (<b>D</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.1</mn> </mrow> </semantics></math>. The corresponding optimal control strategies are presented on the right most column of each figure.</p>
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17 pages, 12791 KiB  
Article
Analysis of Flow and Wear Characteristics of Solid–Liquid Two-Phase Flow in Rotating Flow Channel
by Peng Wang, Xinyu Zhu and Yi Li
Processes 2020, 8(11), 1512; https://doi.org/10.3390/pr8111512 - 21 Nov 2020
Cited by 4 | Viewed by 2512
Abstract
To study the flow characteristics and the wear distribution of pumps at different rotation speeds, a rotating disc with three blades was designed for experiments. Numerical simulations were conducted using a computational fluid dynamics-discrete phase model (CFD–DPM) approach. The experimental and numerical results [...] Read more.
To study the flow characteristics and the wear distribution of pumps at different rotation speeds, a rotating disc with three blades was designed for experiments. Numerical simulations were conducted using a computational fluid dynamics-discrete phase model (CFD–DPM) approach. The experimental and numerical results were compared, and the flow characteristics and wear behaviors were determined. As the speed increased, the particles at the blade working surface aggregated. The particle velocity gradually increased at the outlet of the channel. The severe wear areas were all located in the outlet area of the blade working surface, and the wear area extended toward the inlet area of the blade with increasing speed. The wear rate of the blade surface increased as the speed increased, and an area with a steady wear rate appeared at the outlet area of the blade. When the concentration was more than 8%, the severe wear areas were unchanged at the same speed. When the speed increased, the severe wear areas of the blade produced wear ripples, and the area of the ripples increased with increasing speed. The height difference between the ripples along the flow direction on the blade became larger as the speed increased. Full article
(This article belongs to the Section Energy Systems)
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<p>Schematic view of the experimental bench system. 1. Motor; 2. pump; 3. electromagnetic flow meter; 4. valve; 5. sliding bearing; 6. rotating disc; 7. frequency conversion motor; 8. stirring motor; 9. stirrer; 10. water storage tank; 11. motor support.</p>
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<p>Photographic view of the experimental table.</p>
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<p>(<b>a</b>) Glass particles; (<b>b</b>) particle diameters.</p>
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<p>(<b>a</b>) Laser displacement sensor; (<b>b</b>) measurement method.</p>
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<p>Computational domain of the centrifugal slurry pump.</p>
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<p>Computational domain mesh.</p>
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<p>Mesh independence test.</p>
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<p>Comparison of wear area of blade working face at 500 rpm and 10% concentration.</p>
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<p>Thickness loss rate of No.1 blade working surface at 500 rpm and 10% concentration.</p>
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<p>Particle distribution and particle volume fraction at different rotation speeds at 8% particle mass concentration.</p>
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<p>Wear cloud diagram at 600 rpm at 8% particle mass concentration.</p>
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<p>Abrasion diagram of the working surfaces of blades at different rotation speeds and an 8% particle mass concentration.</p>
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<p>Thickness loss rate at the exit area A of the blade working face at an 8% mass concentration.</p>
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<p>Wear for different particle concentrations at 600 rpm.</p>
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<p>Thickness loss rate at the outlet area A of the blade working surface at 600 rpm.</p>
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<p>Blade wear surface morphology under different working conditions.</p>
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<p>Gully heights of aluminum sheet surfaces at different rotation speeds and 10% mass concentration (unit: µm).</p>
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16 pages, 2778 KiB  
Article
Digital Twinning Process for Stirred Tank Reactors/Separation Unit Operations through Tandem Experimental/Computational Fluid Dynamics (CFD) Simulations
by Blaž Oblak, Simon Babnik, Vivian Erklavec-Zajec, Blaž Likozar and Andrej Pohar
Processes 2020, 8(11), 1511; https://doi.org/10.3390/pr8111511 - 21 Nov 2020
Cited by 6 | Viewed by 5201
Abstract
Computational fluid dynamics simulations (CFD) were used to evaluate mixing in baffled and unbaffled vessels. The Reynolds-averaged Navier−Stokes kε model was implemented in OpenFOAM for obtaining the fluid flow field. The 95% homogenization times were determined by tracer tests. Experimental tests [...] Read more.
Computational fluid dynamics simulations (CFD) were used to evaluate mixing in baffled and unbaffled vessels. The Reynolds-averaged Navier−Stokes kε model was implemented in OpenFOAM for obtaining the fluid flow field. The 95% homogenization times were determined by tracer tests. Experimental tests were conducted by injecting sodium chloride into the vessel and measuring the conductivity with two conductivity probes, while the simulations replicated the experimental conditions with the calculation of the transport of species. It was found that the geometry of the system had a great effect on the mixing time, since the irregular flow distribution, which can be obtained with baffles, can lead to local stagnation zones, which will increase the time needed to achieve the homogenization of the solute. It was also found that measuring local, pointwise concentrations can lead to a high underestimation of the global mixing time required for the homogenization of the entire vessel. Dissolution of sucrose was also studied experimentally and by mathematical modeling. The dissolution of sucrose was found to be kinetically limited and a very good agreement was found between the experiments and the modeling approach. The extent of the applicability of CFD simulations was evaluated for enabling rapid process design via simulations. Full article
(This article belongs to the Special Issue Redesign Processes in the Age of the Fourth Industrial Revolution)
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<p>Simulated local normalized concentration for 2 different experiments. Upper: (experiment F 30 rpm), lower: (experiment F 90 rpm).</p>
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<p>Simulated local normalized concentration for 2 different experiments. Upper: (experiment H 30 rpm), lower: (experiment D4 30 rpm).</p>
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<p>Simulated global mixing times for different stirred tanks configurations and different rpms.</p>
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<p>Graphical presentation of mean velocity on the <span class="html-italic">Z</span>-axis for floor plan of the reactor (<b>a</b>,<b>c</b>) and for the longitudinal section of the reactor (<b>b</b>,<b>d</b>). Presented experiments are C (<b>a</b>,<b>b</b>) and G (<b>c</b>,<b>d</b>).</p>
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<p>Dissolution of sucrose at different mixing vessel configurations and different mixing speeds.</p>
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<p>Experimental and simulation of dissolution profile. Right (<b>b</b>) is D3 with 150 rpm and left (<b>a</b>) is D2 with 150 rpm.</p>
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<p>Experiments E (<b>a</b>,<b>c</b>, unbaffled) and B (<b>b</b>,<b>d</b>, baffled) results for velocity profile and standard deviation (<b>a</b>,<b>b</b>) and for shear rate (<b>c</b>,<b>d</b>).</p>
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<p>Comparison of streamlines for two experiments. B (<b>right</b>) is 1.5 L full with baffles and E (<b>left</b>) is 1 L full without baffles.</p>
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20 pages, 1831 KiB  
Article
Application of a Modeling Tool to Describe Fly Ash Generation, Composition, and Melting Behavior in a Wheat Straw Fired Commercial Power Plant
by Ibai Funcia, Fernando Bimbela, Javier Gil and Luis M. Gandía
Processes 2020, 8(11), 1510; https://doi.org/10.3390/pr8111510 - 20 Nov 2020
Viewed by 2018
Abstract
Ash behavior is a key operational aspect of industrial-scale power generation by means of biomass combustion. In this work, FactSageTM 6.4 software was used to develop and assess three models of wheat straw combustion in a vibrating grate-fired commercial boiler of 16 [...] Read more.
Ash behavior is a key operational aspect of industrial-scale power generation by means of biomass combustion. In this work, FactSageTM 6.4 software was used to develop and assess three models of wheat straw combustion in a vibrating grate-fired commercial boiler of 16 MWth, aiming to describe the inorganic elements release as well as fly ash melting behavior and composition. Simulations were carried out solving four consecutive calculation stages corresponding to the main plant sections. Chemical fractionation was adopted in order to distinguish between reactive, inert and partially reactive biomass fractions. The developed models allow take into account different levels of partial reactivity, values of the temperature for each sub-stage on the grate, and ways to apply entrained streams based on data from the elemental analyses of the fly ashes. To this end, two one-week experimental campaigns were conducted in the plant to carry out the sampling. It has been found that considering chemical fractionation is indispensable to describe the entrainment of solid particles in the gas stream. In addition, the best results are obtained by adopting a small reactivity (2%) of the inert fraction. As for fly ash composition, the concentrations of the major elements showed good agreement with the results from the chemical analyses. In the case of S and Cl, calculations revealed a match with gas cooling effects in the superheaters as well as an entrainment effect. The melting behavior together with the presence of KCl and K2SO4 condensates, point out at possible corrosion phenomena in walls at temperatures of 700–750 °C. Full article
(This article belongs to the Special Issue Progress in Thermochemical Conversion of Solid Fuels)
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<p>Schematic diagram of the 16 MW<sub>th</sub> biomass firing power plant owned by Acciona Energía in Briviesca (Burgos, Spain). See the text (previous paragraph) for descriptions of the acronyms.</p>
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<p>Scheme of the model representing the power plant by means of the main stages and streams involved.</p>
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<p>Total entrainment coefficients (<span class="html-italic">XT</span>) calculated on mass percentage of Al, Ca, Fe, Mg and Si measured in the fly ashes of the power plant by means of the main stages and streams involved.</p>
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<p>Main elements content of the biomass, and fly and bottom (slag) ashes per kg of fed biomass (dry basis).</p>
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<p>Results of the simulations for the fly ash flow rates in stages EQ3 and EQ4 according to the entrainment models GE (<b>top</b>), IE (<b>middle</b>), and TE (<b>bottom</b>). For each model, the four cases considered, as concerns chemical fractionation (CF100, CF102, CF110, and N°CF), are included. Black squares on the vertical axis correspond to the values measured onsite at the plant.</p>
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<p>Evolution with temperatures of condensed K (<b>a</b>), S (<b>b</b>), and Cl (<b>c</b>), as predicted by the GE entrainment model, and condensed Cl according to the TE model (<b>d</b>), considering (CF100) and disregarding (Non CF) chemical fractionation. Black squares correspond to the g of the element per kg of biomass obtained from the elemental analyses of fly ash samples taken onsite at the plant.</p>
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<p>Evolution with temperatures of condensed K (<b>a</b>), S (<b>b</b>), and Cl (<b>c</b>), as predicted by the GE entrainment model, and condensed Cl according to the TE model (<b>d</b>), considering (CF100) and disregarding (Non CF) chemical fractionation. Black squares correspond to the g of the element per kg of biomass obtained from the elemental analyses of fly ash samples taken onsite at the plant.</p>
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<p>Comparison between the predicted SO<sub>2</sub> concentrations in the flue gas from the simulations carried out using the GE and IE models and the experimental ones obtained onsite.</p>
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<p>Ash melting curves obtained for Test 1 (<b>top</b>) and Test 2 (<b>bottom</b>) disregarding (solid line) and considering (dashed line) chemical fractionation (CF100 case, GE model). Horizontal lines indicate the T<sub>15</sub> and T<sub>70</sub> values, respectively.</p>
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10 pages, 4551 KiB  
Article
A Thermal Design of a 1 kW-Class Shell and Tube Methanol Steam Reforming System with Internal Evaporator
by Dongjin Yu, Ngoc Trinh Van, Jinwon Yun and Sangseok Yu
Processes 2020, 8(11), 1509; https://doi.org/10.3390/pr8111509 - 20 Nov 2020
Cited by 2 | Viewed by 3623
Abstract
Due to its low operating temperature, the performance of a methanol steam reformer depends on efficient thermal integration. In particular, the integration of the evaporator is crucial to enhance thermal efficiency. This paper presents two different configurations to utilize thermal energy for evaporation [...] Read more.
Due to its low operating temperature, the performance of a methanol steam reformer depends on efficient thermal integration. In particular, the integration of the evaporator is crucial to enhance thermal efficiency. This paper presents two different configurations to utilize thermal energy for evaporation of methanol/water mixture. The reformer system is composed of a methanol steam reformer, a burner, and two different evaporators such as internal evaporator and external evaporator. Moreover, since the performance of the reforming system strongly depends on thermal utilization, a heat recovery module is designed for methanol reforming system with internal evaporator. The heat duty and steam to carbon ratio (S/C) are the variables for evaluation of its suitability. The experimental results indicate that the internal evaporator with the auxiliary heat recovery module provides stable conditions over wide operating ranges. Full article
(This article belongs to the Special Issue Integration of Carbon Dioxide and Hydrogen Supply Chains)
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<p>Design of methanol steam reformer, evaporator and heat recovery module.</p>
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<p>Experimental apparatus for methanol steam reforming system (<b>a</b>) without auxiliary heat recovery module (AHRM) and (<b>b</b>) with AHRM.</p>
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<p>Methanol steam reformer system.</p>
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<p>Gas concentration and component temperature without AHRM (reference case).</p>
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<p>Efficiency of the reference system with different heat duty.</p>
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<p>Gas concentration and component temperature with AHRM (case of enhance thermal management).</p>
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<p>Efficiency of the internally evaporated system with different heat duty with auxiliary heat recovery module.</p>
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<p>Different steam to carbon ratios for reformer inlet with extra heat recovery module.</p>
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<p>Efficiency of the system for different S/C ratios.</p>
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33 pages, 28015 KiB  
Article
Mathematical Modeling of the Production of Elastomers by Emulsion Polymerization in Trains of Continuous Reactors
by Enrique Saldívar-Guerra, Ramiro Infante-Martínez and José María Islas-Manzur
Processes 2020, 8(11), 1508; https://doi.org/10.3390/pr8111508 - 20 Nov 2020
Cited by 7 | Viewed by 3510
Abstract
A mechanistic model is proposed to describe the emulsion polymerization processes for the production of styrene–butadiene rubber (SBR) and acrylonitrile–butadiene rubber (NBR) elastomers in trains of continuous stirred tank reactors (CSTRs). A single model was used to describe both processes by choosing the [...] Read more.
A mechanistic model is proposed to describe the emulsion polymerization processes for the production of styrene–butadiene rubber (SBR) and acrylonitrile–butadiene rubber (NBR) elastomers in trains of continuous stirred tank reactors (CSTRs). A single model was used to describe both processes by choosing the proper physicochemical parameters of each system. Most of these parameters were taken from literature sources or estimated a priori; only one parameter (the entry rate coefficient) was used as an adjustable value to reproduce the kinetics (mainly conversion), and another parameter (the transfer to polymer rate coefficient) was used to fit the molecular weight distribution (MWD) experimental values from plant data. A 0-1-2 model for the number of particles and for the moments of the MWD was used to represent with more fidelity the compartmentalization effects. The model was based on approaches used in previous emulsion polymerization models published in the literature, with the premise of reaching a compromise between the level of detail, complexity, and practical value. The model outputs along the reactor train included conversion, remaining monomer composition, instantaneous and accumulated copolymer composition, the number of latex particles and particle diameter, polymerization rate, the average number of radicals per particle, average molecular weights, and the number of branches per chain. Full article
(This article belongs to the Special Issue Modeling and Simulation of Polymerization Processes)
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<p>Data flow and computer programs for the implementation of the numerical solution of the mathematical model. Each stage (darker blocks) represents a computer program that is run separately and sequentially. See the text for more details.</p>
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<p>Comparison of plant conversion data with model predictions for two operating conditions. The two simulated operating conditions are indicated as Model 1 and Model 2, See the conditions in <a href="#processes-08-01508-t004" class="html-table">Table 4</a>.</p>
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<p>Comparison of model prediction with plant experimental data for conversion (<b>A</b>), number average molecular weight instantaneous (<span class="html-italic">M</span><sub>n</sub>) (<b>B</b>), MWD dispersity (<b>C</b>), and accumulated copolymer composition <span class="html-italic">F</span><sub>1</sub> (<b>D</b>). The parameter values were those in <a href="#processes-08-01508-t003" class="html-table">Table 3</a>, and the reaction conditions were similar to those in <a href="#processes-08-01508-t004" class="html-table">Table 4</a>.</p>
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<p>Effect of variation of surfactant (from −45% to +45%) on conversion (<b>A</b>), remaining monomer feed composition <span class="html-italic">f</span><sub>1</sub> (<b>B</b>), instantaneous copolymer composition (<span class="html-italic">F</span><sub>1,inst</sub>) (<b>C</b>), and accumulated copolymer composition <span class="html-italic">F</span><sub>1</sub> (<b>D</b>) along the reactor train with respect to the base case in SBR production. All compositions are weight fractions.</p>
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<p>Effect of variation of surfactant (from −45% to +45%) on number of particles <span class="html-italic">N</span><sub>p</sub> (<b>A</b>), average number of radicals <math display="inline"><semantics> <mover accent="true"> <mi>n</mi> <mo>˜</mo> </mover> </semantics></math> (<b>B</b>), reaction rate <span class="html-italic">R</span><sub>p</sub> (<b>C</b>), and particle diameter <span class="html-italic">D</span><sub>p</sub> (<b>D</b>) along the reactor train with respect to the base case in SBR production.</p>
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<p>Effect of variation of surfactant (from −45% to +45%) on the number-average molecular weight <span class="html-italic">M</span><sub>n</sub> (<b>A</b>), weight-average molecular weight <span class="html-italic">M</span><sub>w</sub> (<b>B</b>), and MWD dispersity (<b>C</b>) along the reactor train with respect to the base case in SBR production.</p>
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<p>Number of particles with zero (F_0), one (F_1), and two (F_2) radicals per L of water at each reactor for the SBR base case. (<b>A</b>) Linear scale; (<b>B</b>) log scale.</p>
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<p>Effect of variation of chain transfer agents (CTAs) (from −30% to +45%) on the conversion (<b>A</b>), number-average molecular weight <span class="html-italic">M</span><sub>n</sub> (<b>B</b>), weight-average molecular weight <span class="html-italic">M</span><sub>w</sub> (<b>C</b>), and MWD dispersity (<b>D</b>) along the reactor train with respect to the base case in SBR production.</p>
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<p>Effect of suppressing the side stream of CTAs of the base case on the conversion (<b>A</b>), number-average molecular weight <span class="html-italic">M</span><sub>n</sub> (<b>B</b>), MWD dispersity (<b>C</b>), and number of branches per chain (<b>D</b>) along the reactor train with respect to the base case (corrected) in SBR production.</p>
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<p>Effect of variation of surfactant (from −45% to +45%) on conversion (<b>A</b>), remaining monomer feed composition <span class="html-italic">f</span><sub>1</sub> (<b>B</b>), instantaneous copolymer composition (<span class="html-italic">F</span><sub>1,inst</sub>) (<b>C</b>), and accumulated copolymer composition <span class="html-italic">F</span><sub>1</sub> (<b>D</b>) along the reactor train with respect to the base case in NBR production. All compositions are weight fractions.</p>
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<p>Effect of variation of surfactant (from −45% to +45%) on number of particles <span class="html-italic">N</span><sub>p</sub> (<b>A</b>), average number of radicals <math display="inline"><semantics> <mover accent="true"> <mi>n</mi> <mo>˜</mo> </mover> </semantics></math> (<b>B</b>), reaction rate <span class="html-italic">R</span><sub>p</sub> (<b>C</b>), and particle diameter <span class="html-italic">D</span><sub>p</sub> (<b>D</b>) along the reactor train with respect to the base case in NBR production.</p>
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<p>Effect of variation of surfactant (from −45% to +45%) on the number-average molecular weight <span class="html-italic">M</span><sub>n</sub> (<b>A</b>), weight-average molecular weight <span class="html-italic">M</span><sub>w</sub> (<b>B</b>), and MWD dispersity (<b>C</b>) along the reactor train with respect to the base case in NBR production.</p>
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<p>Effect of variation of CTAs (from −45% to +45%) on the conversion (<b>A</b>), number-average molecular weight <span class="html-italic">M</span><sub>n</sub> (<b>B</b>), weight-average molecular weight <span class="html-italic">M</span><sub>w</sub> (<b>C</b>), and MWD dispersity (<b>D</b>) along the reactor train with respect to the base case in NBR production.</p>
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<p>Effect of the operation of the reactor train for NBR production with (corrected) and without a side stream of additional AN in R7 (20% with respect to the AN feed in R1) on conversion (<b>A</b>) and instantaneous butadiene copolymer composition (<span class="html-italic">F</span><sub>1,inst</sub>) and accumulated butadiene copolymer composition (<span class="html-italic">F</span><sub>1</sub>) (<b>B</b>). The initial monomer feed was <span class="html-italic">f</span><sub>1</sub> = 0.68 (wt. basis).</p>
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<p>Effect of the operation of the reactor train for NBR production with (corrected) and without a side stream of additional Bd in R7 (20% with respect to the Bd feed in R1) on conversion (<b>A</b>) and instantaneous butadiene copolymer composition (<span class="html-italic">F</span><sub>1,inst</sub>) and accumulated butadiene copolymer composition (<span class="html-italic">F</span><sub>1</sub>) (<b>B</b>). The initial monomer feed is <span class="html-italic">f</span><sub>1</sub> = 0.5 (wt. basis).</p>
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23 pages, 4962 KiB  
Article
Impact of Process Parameters and Bulk Properties on Quality of Dried Hops
by Sharvari Raut, Gardis J. E. von Gersdorff, Jakob Münsterer, Klaus Kammhuber, Oliver Hensel and Barbara Sturm
Processes 2020, 8(11), 1507; https://doi.org/10.3390/pr8111507 - 20 Nov 2020
Cited by 14 | Viewed by 3684
Abstract
Hops are critical to the brewing industry. In commercial hop drying, a large bulk of hops is dried in multistage kilns for several hours. This affects the drying behavior and alters the amount and chemical composition of the hop oils. To understand these [...] Read more.
Hops are critical to the brewing industry. In commercial hop drying, a large bulk of hops is dried in multistage kilns for several hours. This affects the drying behavior and alters the amount and chemical composition of the hop oils. To understand these changes, hops of the var. Hallertauer Tradition were dried in bulks of 15, 25 and 35 kg/m² at 60 °C and 0.35 m/s. Additionally, bulks of 25 kg/m² were also dried at 65 °C and 0.45 m/s to assess the effect of change in temperature and velocity, respectively. The results obtained show that bulk weights significantly influence the drying behavior. Classification based on the cone size reveals 45.4% medium cones, 41.2% small cones and 8.6% large cones. The highest ΔE value of 6.3 and specific energy consumption (113,476 kJ/kgH2O) were observed for the 15 kg/m² bulk. Increasing the temperature from 60 °C to 65 °C increased the oil yield losses by about 7% and myrcene losses by 22%. The results obtained show that it is important to define and consider optimum bulk and process parameters, to optimize the hop drying process to improve the process efficiency as well the product quality. Full article
(This article belongs to the Special Issue Advances in Postharvest Process Systems)
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<p>Schematic of hops dryer (adapted and revised from [<a href="#B9-processes-08-01507" class="html-bibr">9</a>]).</p>
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<p>Hop cone size variation in varieties (<b>a</b>) Hallertauer Tradition, (<b>b</b>) Hallertauer Herkules [<a href="#B9-processes-08-01507" class="html-bibr">9</a>].</p>
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<p>Schematic of RGB camera imaging system for hops.</p>
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<p>Temperature (symbol and line) and humidity (symbol) profile for different bulk weight and different process settings.</p>
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<p>Moisture ratio (MR) vs. time (min) for hops with three different bulk weights dried at 60 °C and 0.35 m/s.</p>
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<p>Moisture ratio (MR) vs. time (min) for hops dried at (<b>a</b>) 0.35 m/s and 0.45 m/s at 60 °C (<b>b</b>) 60 °C and 65 °C at 0.35 m/s.</p>
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<p>Air channel formation in a 15 kg/m<sup>2</sup> bulk of hops.</p>
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<p>Amount of oil in L/kg hops extracted for samples extracted at 0 min and 210 min for hop cones dried at 60 °C and 65 °C at 0.35 m/s.</p>
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<p>Results obtained from gas chromatography analysis for myrcene in g/kg hops at 0 min and 210 min for hop cones dried at 60 °C and 65 °C at 0.35 m/s.</p>
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<p>Results obtained from gas chromatography analysis for linalool in g/kg hops at 0 min and 210 min for hop cones dried at 60 °C and 65 °C at 0.35 m/s.</p>
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<p>Results obtained from gas chromatography analysis for β-caryophyllene in g/kg hops at 0 min and 210 min for hop cones dried at 60 °C and 65 °C at 0.35 m/s.</p>
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<p>Results obtained from gas chromatography analysis for humulene in g/kg hops at 0 min and 210 min for hop cones dried at 60 °C and 65 °C at 0.35 m/s.</p>
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<p>Correlation between CIE L*a*b* values between chromameter and RGB camera system.</p>
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<p>ΔE as a function of moisture ratio (MR) for hops dried under different process settings and different bulk weights.</p>
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<p>Specific energy requirement for three different hop bulks dried at 60 °C and 0.35 m/s.</p>
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<p>Specific energy requirements for 25 kg/m<sup>2</sup> bulk dried at (<b>a</b>) 0.35 m/s and 0.45 m/s at 60 °C (<b>b</b>) 60 °C and 65 °C at 0.35 m/s.</p>
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17 pages, 8415 KiB  
Article
The Effect of Root Clearance on Mechanical Energy Dissipation for Axial Flow Pump Device Based on Entropy Production
by Yanjun Li, Yunhao Zheng, Fan Meng and Majeed Koranteng Osman
Processes 2020, 8(11), 1506; https://doi.org/10.3390/pr8111506 - 20 Nov 2020
Cited by 18 | Viewed by 2678
Abstract
The axial flow pump is a low head, high discharge pump usually applicable in drainage and irrigation facilities. A certain gap should be reserved between the impeller blade root and the impeller hub to ensure the blade adjustability to broaden the high-efficiency area. [...] Read more.
The axial flow pump is a low head, high discharge pump usually applicable in drainage and irrigation facilities. A certain gap should be reserved between the impeller blade root and the impeller hub to ensure the blade adjustability to broaden the high-efficiency area. The pressure difference between its blade surface induces leakage flow in the root clearance region, which decreases hydraulic performance and operational stability. Therefore, this study was carried out to investigate the effect of root clearance on mechanical energy dissipation using numerical simulation and entropy production methods. The numerical model was validated with an external characteristics test, and unsteady flow simulations were conducted on the axial flow pump under four different root clearance radii. The maximum reductions of 15.5% and 6.8% for head and hydraulic efficiency are obtained for the largest root clearance of 8 mm, respectively. The dissipation based on entropy theory consists of indirect dissipation and neglectable direct dissipation. The leakage flow in the root clearance led to the distortion of the impeller’s flow pattern, and the indirect dissipation rate and overall dissipation of the impeller increased with increasing root clearance radius. The inflow pattern in the diffuser was also distorted by leakage flow. The diffuser’s overall dissipation, indirect dissipation rate on the blade surface, and indirect dissipation rate near inlet increased with increasing root clearance radius. The research could serve as a theoretical reference for the axial flow pump’s root clearance design for performance improvement and operational stability. Full article
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<p>3D model of the axial flow pump device.</p>
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<p>Mesh of axial flow pump device.</p>
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<p>Grid independence of pump device without root clearance.</p>
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<p>Photo of the axial flow pump device.</p>
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<p>Comparison of simulated pump performance under different root clearance radii.</p>
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<p>Comparison of pump performance between simulated data and test results (<span class="html-italic">R<sub>t</sub></span> = 0 mm).</p>
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<p>Comparison of simulated (<b>a</b>) hydraulic efficiency and (<b>b</b>) head under different root clearance radii.</p>
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<p>Distribution of (<b>a</b>) power loss due to indirect dissipation and (<b>b</b>) power loss due to direct dissipation of different hydraulic components without root clearance under five flow rates.</p>
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<p>Distribution of power loss due to indirect dissipation at 1.0 <span class="html-italic">Q</span><sub>des</sub> for varying root clearances <span class="html-italic">(R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm, 8 mm).</p>
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<p>The cylindrical cross-section for impeller and diffuser.</p>
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<p>Distribution of velocity vector at Span = 0.015 in impeller passage with varying root clearances <span class="html-italic">(R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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<p>Velocity contour at Span = 0.015 in impeller passage with varying root clearances (<span class="html-italic">R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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<p>Distribution of the indirect dissipation rate at Span = 0.015 in the impeller passage with varying root clearances (<span class="html-italic">R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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<p>Standard deviation of the velocity distribution at Span = 0.015 in the impeller passage with varying root clearances (<span class="html-italic">R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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<p>Velocity vectors at Span = 0.015 in guide vane passage with varying root clearances (<span class="html-italic">R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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<p>Indirect dissipation rate at Span = 0.015 in guide vane passage with varying root clearances (<span class="html-italic">R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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<p>Distribution of indirect dissipation rate (<b>a</b>) from diffuser inlet and (<b>b</b>) from diffuser outlet on the surface of diffuser blades with varying root clearances (<span class="html-italic">R<sub>t</sub></span> = 0 mm, 2.7 mm, 5 mm, 8 mm; <span class="html-italic">Q</span> = 1.0 <span class="html-italic">Q</span><sub>des</sub>).</p>
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26 pages, 873 KiB  
Article
An Agricultural Products Supply Chain Management to Optimize Resources and Carbon Emission Considering Variable Production Rate: Case of Nonperishable Corps
by Mohammed Alkahtani, Muhammad Omair, Qazi Salman Khalid, Ghulam Hussain and Biswajit Sarkar
Processes 2020, 8(11), 1505; https://doi.org/10.3390/pr8111505 - 20 Nov 2020
Cited by 10 | Viewed by 3529
Abstract
The management of the man–machine interaction is essential to achieve a competitive advantage among production firms and is more highlighted in the processing of agricultural products. The agricultural industry is underdeveloped and requires a transformation in technology. Advances in processing agricultural products (agri-product) [...] Read more.
The management of the man–machine interaction is essential to achieve a competitive advantage among production firms and is more highlighted in the processing of agricultural products. The agricultural industry is underdeveloped and requires a transformation in technology. Advances in processing agricultural products (agri-product) are essential to achieve a smart production rate with good quality and to control waste. This research deals with modelling of a controllable production rate by a combination of the workforce and machines to minimize the total cost of production. The optimization of the carbon emission variable and management of the imperfection in processing makes the model eco-efficient. The perishability factor in the model is ignored due to the selection of a single sugar processing firm in the supply chain with a single vendor for the pragmatic application of the proposed research. A non-linear production model is developed to provide an economic benefit to the firms in terms of the minimum total cost with variable cycle time, workforce, machines, and plant production rate. A numerical experiment is performed by utilizing the data set of the agri-processing firm. A derivative free approach, i.e., algebraic approach, is utilized to find the best solution. The sensitivity analysis is performed to support the managers for the development of agricultural product supply chain management (Agri-SCM). Full article
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<p>The inventory diagram of the agricultural supply chain management (Agri-SCM).</p>
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<p>The sensitivity of total cost of Agri-SCM with respect to the cost parameters.</p>
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31 pages, 22328 KiB  
Review
Fractionation and Characterization of Petroleum Asphaltene: Focus on Metalopetroleomics
by Fang Zheng, Quan Shi, Germain Salvato Vallverdu, Pierre Giusti and Brice Bouyssiere
Processes 2020, 8(11), 1504; https://doi.org/10.3390/pr8111504 - 20 Nov 2020
Cited by 47 | Viewed by 7249
Abstract
Asphaltenes, as the heaviest and most polar fraction of petroleum, have been characterized by various analytical techniques. A variety of fractionation methods have been carried out to separate asphaltenes into multiple subfractions for further investigation, and some of them have important reference significance. [...] Read more.
Asphaltenes, as the heaviest and most polar fraction of petroleum, have been characterized by various analytical techniques. A variety of fractionation methods have been carried out to separate asphaltenes into multiple subfractions for further investigation, and some of them have important reference significance. The goal of the current review article is to offer insight into the multitudinous analytical techniques and fractionation methods of asphaltene analysis, following an introduction with regard to the morphologies of metals and heteroatoms in asphaltenes, as well their functions on asphaltene aggregation. Learned lessons and suggestions on possible future work conclude the present review article. Full article
(This article belongs to the Special Issue Heavy Oils Conversion Processes)
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<p>Laboratory sample of asphaltenes extracted from crude oil in n-heptane (<b>left</b>) and n-pentane (<b>right</b>).</p>
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<p>Chromatograms obtained by gel-permeation chromatography (GPC) inductively coupled plasma (ICP) MS of collected and reinjected (<b>a</b>) high-molecular-weight (HMW), (<b>b</b>) medium-molecular-weight (MMW), and (<b>c</b>) low-molecular-weight (LMW) fractions [<a href="#B47-processes-08-01504" class="html-bibr">47</a>].</p>
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<p>Atomic force microscopy (AFM) images of coal-derived asphaltenes and petroleum asphaltenes [<a href="#B50-processes-08-01504" class="html-bibr">50</a>].</p>
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<p>Modified Yen model [<a href="#B195-processes-08-01504" class="html-bibr">195</a>].</p>
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<p>Supramolecular assembly model of asphaltene proposed by Murray R. Gray et al. [<a href="#B179-processes-08-01504" class="html-bibr">179</a>].</p>
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13 pages, 4171 KiB  
Article
Preparation of Hybrid Polyaniline/Nanoparticle Membranes for Water Treatment Using an Inverse Emulsion Polymerization Technique under Sonication
by Itamar Chajanovsky and Ran Y. Suckeveriene
Processes 2020, 8(11), 1503; https://doi.org/10.3390/pr8111503 - 20 Nov 2020
Cited by 7 | Viewed by 2615
Abstract
This manuscript describes a novel in situ interfacial dynamic inverse emulsion polymerization process under sonication of aniline in the presence of carbon nanotubes (CNT) and graphene nanoparticles in ethanol. This polymerization method is simple and very rapid (up to 10 min) compared to [...] Read more.
This manuscript describes a novel in situ interfacial dynamic inverse emulsion polymerization process under sonication of aniline in the presence of carbon nanotubes (CNT) and graphene nanoparticles in ethanol. This polymerization method is simple and very rapid (up to 10 min) compared to other techniques reported in the literature. During polymerization, the nanoparticles are coated with polyaniline (PANI), forming a core-shell structure, as confirmed by high-resolution scanning electron microscopy (HRSEM) and Fourier-Transform Infrared (FTIR) measurements. The membrane pore sizes range between 100–200 nm, with an average value of ~119 ± 28.3 nm. The film resistivity decreased when treated with alcohol, and this behavior was used for selection of the most efficient alcohol as a solvent for this polymerization technique. The membrane permeability of the PANI grafted CNT was lower than the CNT reference, thus demonstrating better membranal properties. As measured by water permeability, these are ultrafiltration membranes. An antimicrobial activity test showed that whereas the reference nanoparticle Bucky paper developed a large bacterial colony, the PANI grafted CNT sample had no bacterial activity. The thicker, 2.56 mm membranes exhibited high salt removal properties at a low pressure drop. Such active membranes comprise a novel approach for future water treatment applications. Full article
(This article belongs to the Special Issue Polymerization Technologies in the Presence of Nanoparticles)
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<p>Film resistivity with different solvents washes.</p>
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<p>Fourier-Transform Infrared (FTIR) spectra of neat CNT, PANI/CNT physical mixture, and PANI grafted CNT Bucky paper.</p>
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<p>(<b>a</b>) Thermal gravimetric analyzer (TGA) thermograms of neat CNT and PANI grafted CNT Bucky paper. (<b>b</b>) derivative thermal gravimetric (DTG) thermograms of neat CNT and PANI grafted CNT Bucky paper.</p>
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<p>High-resolution scanning electron microscopy (HRSEM) images of PANI/CNT Bucky paper.</p>
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<p>Antimicrobial activity test, using the colony counting technique for the (<b>a</b>) reference CNT, (<b>b</b>) PANI/CNT physical mixture, and (<b>c</b>) the PANI-coated CNT membranes. Red indicated no bacteria populations at all, whereas blue indicated intense bacterial activity.</p>
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<p>Distilled water flux properties of the reference CNT (triangle), PANI/CNT physical mixture (X), and PANI-coated CNT (circle) membranes.</p>
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<p>Distilled water flux properties of the 25 mL (blue), 50 mL (orange), and 100 mL (grey) PANI-coated CNT membranes at the trans-membranal pressure drop of 4 bars.</p>
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<p>Distilled water flux properties of the 100 mL PANI-coated CNT membranes at different pressures.</p>
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<p>Salt removal of the PANI-coated CNT membranes.</p>
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15 pages, 4810 KiB  
Article
Catalytic Performance of Lanthanum Promoted Ni/ZrO2 for Carbon Dioxide Reforming of Methane
by Mahmud S. Lanre, Ahmed S. Al-Fatesh, Anis H. Fakeeha, Samsudeen O. Kasim, Ahmed A. Ibrahim, Abdulrahman S. Al-Awadi, Attiyah A. Al-Zahrani and Ahmed E. Abasaeed
Processes 2020, 8(11), 1502; https://doi.org/10.3390/pr8111502 - 20 Nov 2020
Cited by 22 | Viewed by 3110
Abstract
Nickel catalysts supported on zirconium oxide and modified by various amounts of lanthanum with 10, 15, and 20 wt.% were synthesized for CO2 reforming of methane. The effect of La2O3 as a promoter on the stability of the catalyst, [...] Read more.
Nickel catalysts supported on zirconium oxide and modified by various amounts of lanthanum with 10, 15, and 20 wt.% were synthesized for CO2 reforming of methane. The effect of La2O3 as a promoter on the stability of the catalyst, the amount of carbon formed, and the ratio of H2 to CO were investigated. In this study, we observed that promoting the catalyst with La2O3 enhanced catalyst activities. The conversions of the feed, i.e., methane and carbon dioxide, were in the order 10La2O3 > 15La2O3 > 20La2O3 > 0La2O3, with the highest conversions being about 60% and 70% for both CH4 and CO2 respectively. Brunauer–Emmett–Teller (BET) analysis showed that the surface area of the catalysts decreased slightly with increasing La2O3 doping. We observed that 10% La2O3 doping had the highest specific surface area (21.6 m2/g) and the least for the un-promoted sample. The higher surface areas of the promoted samples relative to the reference catalyst is an indication of the concentration of the metals at the mouths of the pores of the support. XRD analysis identified the different phases available, which ranged from NiO species to the monoclinic and tetragonal phases of ZrO2. Temperature programmed reduction (TPR) analysis showed that the addition of La2O3 lowered the activation temperature needed for the promoted catalysts. The structural changes in the morphology of the fresh catalyst were revealed by microscopic analysis. The elemental compositions of the catalyst, synthesized through energy dispersive X-ray analysis, were virtually the same as the calculated amount used for the synthesis. The thermogravimetric analysis (TGA) of spent catalysts showed that the La2O3 loading of 10 wt.% contributed to the gasification of carbon deposits and hence gave about 1% weight-loss after a reaction time of 7.5 h at 700 °C. Full article
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<p>Nitrogen physisorption isotherms of 5Ni-ZrO<sub>2</sub>, as well as lanthanum-promoted 5Ni-ZrO<sub>2</sub> samples.</p>
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<p>H<sub>2</sub>-temperature programmed reduction (TPR) profiles of 5Ni-ZrO<sub>2</sub> and La<sub>2</sub>O<sub>3</sub>-promoted 5Ni-ZrO<sub>2</sub> catalysts.</p>
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<p>Temperature-programmed desorption (TPD)-CO<sub>2</sub> profiles of 5Ni-ZrO<sub>2</sub> and lanthanum-promoted 5Ni-ZrO<sub>2</sub> catalysts.</p>
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<p>X-ray diffraction (XRD) patterns for 5Ni-ZrO<sub>2</sub> and La<sub>2</sub>O<sub>3</sub>-promoted 5Ni-ZrO<sub>2</sub> catalysts.</p>
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<p>Conversion of (<b>A</b>) CH<sub>4</sub> and (<b>B</b>) CO<sub>2</sub> at 700 °C, one atmosphere, and GHSV = 42 l/h/g<sub>cat</sub>. of 5Ni-ZrO<sub>2</sub> and La<sub>2</sub>O<sub>3</sub>-promoted 5Ni-ZrO<sub>2</sub> catalyst; (<b>C</b>) H<sub>2</sub>/CO ratio.</p>
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<p>TEM image of (<b>A</b>) fresh 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst, (<b>B</b>) fresh 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst, (<b>C</b>) used 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst, and (<b>D</b>) used 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst.</p>
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<p>SEM image of (<b>A</b>) fresh 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub>catalyst, (<b>B</b>) fresh 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst, (<b>C</b>) used 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst, and (<b>D</b>) used 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> catalyst.</p>
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<p>The Energy Dispersive X-ray (EDX) analysis of 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub>.</p>
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<p>Thermogravimetric analysis (TGA) curves quantify the amount of carbon deposited over the used catalysts.</p>
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<p>The stability test for 5Ni-10 La<sub>2</sub>O<sub>3</sub>-ZrO<sub>2</sub> for 67 h at 1 atm, 700 °C, and GHSV = 42 l/(h.g<sub>cat</sub>.).</p>
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10 pages, 2253 KiB  
Article
The Carbon-Coated ZnCo2O4 Nanowire Arrays Pyrolyzed from PVA for Enhancing Lithium Storage Capacity
by Wenjia Zhao, Zhaoping Shi, Yongbing Qi and Jipeng Cheng
Processes 2020, 8(11), 1501; https://doi.org/10.3390/pr8111501 - 20 Nov 2020
Cited by 5 | Viewed by 2465
Abstract
In this paper, ZnCo2O4 nanowire arrays with a uniform carbon coating were introduced when polyvinyl alcohol (PVA) served as the carbon source. The coating process was completed by a facile bath method in PVA aqueous solution and subsequent pyrolyzation. The [...] Read more.
In this paper, ZnCo2O4 nanowire arrays with a uniform carbon coating were introduced when polyvinyl alcohol (PVA) served as the carbon source. The coating process was completed by a facile bath method in PVA aqueous solution and subsequent pyrolyzation. The PVA-derived carbon-coated ZnCo2O4 nanowire array composites can be used directly as the binder-free and self-supported anode materials for lithium-ion batteries. In the carbon-coated ZnCo2O4 composites, the carbon layer carbonized from PVA can accelerate the electron transfer and accommodate the volume swing during the cycling process. The lithium storage properties of the carbon-coated ZnCo2O4 composites are investigated. It is believed that the novel carbon-coating method is universal and can be applied to other nanoarray materials. Full article
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<p>XRD patterns of (<b>a</b>) ZnCo<sub>2</sub>O<sub>4</sub> and (<b>b</b>) carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> samples.</p>
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<p>(<b>a</b>) A SEM image, (<b>b</b>) a TEM image of carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> nanowires, (<b>c</b>,<b>d</b>) partially enlarged TEM images of uncoated and carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> nanowires, compositional line profiles across (<b>e</b>) the uncoated and (<b>f</b>) carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> nanowires probed by EDX spectroscopy, (<b>g</b>) HRTEM image, (<b>h</b>) SAED patterns of the carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> nanowires.</p>
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<p>(<b>a</b>) Typical Raman spectrum of the uncoated and carbon-coated ZnCo<sub>2</sub>O<sub>4</sub>, (<b>b</b>) TGA analysis of the carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> nanocomposites.</p>
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<p>The first three CV curves of carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> array electrodes between 0.0 V to 3.0 V versus Li/Li<sup>+</sup> at a scan rate of 0.1 mV·s<sup>−1</sup>.</p>
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<p>(<b>a</b>) The comparison of cycling performance of carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> pyrolyzed from PVA solutions with pristine ZnCo<sub>2</sub>O<sub>4</sub> electrodes; (<b>b</b>) charge-discharge curves for lithium-ion batteries; (<b>c</b>) rate performance of the carbon-coated ZnCo<sub>2</sub>O<sub>4</sub> pyrolyzed from PVA solution; (<b>d</b>) Nyquist plots of the carbon-coated and pristine ZnCo<sub>2</sub>O<sub>4</sub> electrodes from 100 kHz to 0.01 Hz.</p>
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8 pages, 592 KiB  
Article
Stability of Plasma Protein Composition in Dried Blood Spot during Storage
by Kristina A. Malsagova, Alexander A. Stepanov, Arthur T. Kopylov, Dmitry V. Enikeev, Natalia V. Potoldykova, Alexander A. Izotov, Tatyana V. Butkova and Anna L. Kaysheva
Processes 2020, 8(11), 1500; https://doi.org/10.3390/pr8111500 - 20 Nov 2020
Cited by 5 | Viewed by 3163
Abstract
Dried blood spot (DBS) technology has become a promising utility for the transportation and storage of biological fluids aimed for the subsequent clinical analysis. The basis of the DBS method is the adsorption of the components of a biological sample onto the surface [...] Read more.
Dried blood spot (DBS) technology has become a promising utility for the transportation and storage of biological fluids aimed for the subsequent clinical analysis. The basis of the DBS method is the adsorption of the components of a biological sample onto the surface of a membrane carrier, followed by drying. After drying, the molecular components of the biosample (nucleic acids, proteins, and metabolites) can be analyzed using modern omics, immunological, or genomic methods. In this work, we investigated the safety of proteins on a membrane carrier by tryptic components over time and at different temperatures (+4, 0, 25 °C) and storage (0, 7, 14, and 35 days). It was shown that the choice of a protocol for preliminary sample preparation for subsequent analytical molecular measurements affects the quality of the experimental results. The protocol for preliminary preparation of a biosample directly in a membrane carrier is preferable compared to the protocol with an additional stage of elution of molecular components before the sample preparation procedures. It was revealed that the composition of biosamples remains stable at a temperature of −20 and +4 °C for 35 days of storage, and at +25 °C for 14 days. Full article
(This article belongs to the Special Issue Advances in Nanomaterials for Selective Adsorption)
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<p>Procedure for filling the membrane carrier with a blood plasma sample.</p>
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<p>Diagram of the change in the relative intensity of the mass spectrometric signal depending on the variant of the protocol for preliminary preparation of the biological sample, on the temperature (−20, +4, +25 °C) for the 1st and 35th days of storage of dried blood spot (DBS) biological samples.</p>
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19 pages, 7478 KiB  
Article
Influence of Gasoline Addition on Biodiesel Combustion in a Compression-Ignition Engine with Constant Settings
by Wojciech Tutak and Arkadiusz Jamrozik
Processes 2020, 8(11), 1499; https://doi.org/10.3390/pr8111499 - 19 Nov 2020
Cited by 2 | Viewed by 2366
Abstract
This paper presents results of investigation of co-combustion process of biodiesel with gasoline, in form of mixture and using dual fuel technology. The main objective of this work was to show differences in both combustion systems of the engine powered by fuels of [...] Read more.
This paper presents results of investigation of co-combustion process of biodiesel with gasoline, in form of mixture and using dual fuel technology. The main objective of this work was to show differences in both combustion systems of the engine powered by fuels of different reactivity. This paper presents parameters of the engine and the assessment of combustion stability. It turns out that combustion process of biodiesel was characterized by lower ignition delay compared to diesel fuel combustion. For 0.54 of gasoline energetic fraction, the ignition delay increased by 25% compared to the combustion of the pure biodiesel, but for dual fuel technology for 0.95 of gasoline fraction it was decreased by 85%. For dual fuel technology with the increase in gasoline fraction, the specific fuel consumption (SFC) was decreased for all analyzed fractions of gasoline. In the case of blend combustion, the SFC was increased in comparison to dual fuel technology. An analysis of spread of ignition delay and combustion duration was also presented. The study confirmed that it is possible to co-combust biodiesel with gasoline in a relatively high energetic fraction. For the blend, the ignition delay was up to 0.54 and for dual fuel it was near to 0.95. Full article
(This article belongs to the Special Issue Processes for Biofuel, Biogas and Resource Recovery from Biomass)
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<p>Power systems of dual fuel engine, (<b>a</b>) supply by blend of fuels, (<b>b</b>) supply by two separate systems.</p>
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<p>Diagram of the experimental setup. 1—engine, 2—diesel fuel injector, 3—gasoline fuel injector, 4—in cylinder pressure sensor, 5—intake air flowmeter, 6—air filter, 7—cooling fan, 8—exhaust gases temperature sensor, 9—PC with data acquisition system, 10—crank angle sensor. (<b>a</b>) experimental setup, (<b>b</b>) the test engine.</p>
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<p>Doses of fuel for various energetic fraction used during the tests, (<b>a</b>) blend, (<b>b</b>) dual fuel.</p>
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<p>Pressure courses for blends (<b>a</b>) and dual fuel (<b>b</b>) combustion mode.</p>
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<p>Pressure rise rate for blends (<b>a</b>) and dual fuel (<b>b</b>) combustion modes.</p>
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<p>Heat release rate for blends (<b>a</b>) and dual fuel (<b>b</b>) combustion modes.</p>
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<p>The impact of combustion mode on pressure and heat release rate in the case of biodiesel-gasoline combustion, (<b>a</b>) ~10% of gasoline, (<b>b</b>) ~20% of gasoline, (<b>c</b>) ~30% of gasoline, (<b>d</b>) ~40% of gasoline, (<b>e</b>) ~50% of gasoline.</p>
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<p>Specific fuel consumption (SFC) (<b>a</b>) and exhaust gases temperature (T_exh) (<b>b</b>).</p>
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<p>Cycle variation of engine powered by BG blend (<b>a</b>) and as DF engine (<b>b</b>).</p>
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<p>Cycle variation of engine powered by blend and as dual fuel engine.</p>
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<p>Results of peak pressure vs. IMEP are presented for blends (<b>a</b>) and dual fuel (<b>b</b>) combustion modes.</p>
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<p>The position of the peak pressure after TDC for blends (<b>a</b>) and dual fuel (<b>b</b>) combustion modes.</p>
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<p>Probability density (PD) of IMEP for biodiesel-gasoline combustion.</p>
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<p>Stages of combustion, ignition delay (<b>a</b>) and combustion duration (<b>b</b>).</p>
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<p>Stability of heat release.</p>
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<p>Emission of unburned hydrocarbons (HC) (<b>a</b>) and nitrogen oxides (NO<sub>x</sub>) (<b>b</b>).</p>
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<p>Emission of carbon monoxides (CO) (<b>a</b>) and excess air ratio (<b>b</b>).</p>
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<p>Soot emission changes for dual fuel and blend mode.</p>
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17 pages, 5990 KiB  
Article
Auto-Aspirated DAF Sparger Study on Flow Hydrodynamics, Bubble Generation and Aeration Efficiency
by Dmitry Vladimirovich Gradov, Andrey Saren, Janne Kauppi, Kari Ullakko and Tuomas Koiranen
Processes 2020, 8(11), 1498; https://doi.org/10.3390/pr8111498 - 19 Nov 2020
Cited by 3 | Viewed by 3725
Abstract
A novel auto-aspirated sparger is examined experimentally in a closed-loop reactor (CLR) at lab scale using particle image velocimetry, high-speed camera and oxygen mass transfer rate measurements. State-of-the-art 3D printing technology was utilized to develop the sparger design in stainless steel. An insignificant [...] Read more.
A novel auto-aspirated sparger is examined experimentally in a closed-loop reactor (CLR) at lab scale using particle image velocimetry, high-speed camera and oxygen mass transfer rate measurements. State-of-the-art 3D printing technology was utilized to develop the sparger design in stainless steel. An insignificant change in the bubble size distribution was observed along the aerated flow, proving the existence of a low coalescence rate in the constraint domain of the CLR pipeline. The studied sparger created macrobubbles evenly dispersed in space. In pure water, the produced bubble size distribution from 190 to 2500 μm is controlled by liquid flow rate. The bubble size dynamics exhibited a power-law function of water flow rate approaching a stable minimum bubble size, which was attributed to the ratio of the fast-growing energy of the bubble surface tension over the kinetic energy of the stream. Potentially, the stream energy can efficiently disperse higher gas flow rates. The oxygen transfer rate was rapid and depended on the water flow rate. The aeration efficiency below 0.4 kW/m3 was superior to the commonly used aerating apparatuses tested at lab scale. The efficient gas dissolution technology has potential in water treatment and carbon capture processes applications. Full article
(This article belongs to the Special Issue Process Intensification in Chemical Reaction Engineering)
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<p>Liquid ejector principle (<b>left</b>) (modified from [<a href="#B20-processes-08-01498" class="html-bibr">20</a>]) and the auto-aspirated sparger printed in metal with labelled inlet streams (<b>right</b>). Sparger length: 100 mm, sparger diameter: 60 mm. <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>L</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>G</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mi>L</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mi>G</mi> </msub> </mrow> </semantics></math> are the pressure and flow rates of liquid and gas streams.</p>
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<p>Laboratory CLR equipped with the DAF sparger.</p>
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<p>Schematic representation of stereoscopic PIV set-up.</p>
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<p>Recorded images positioning along the flow (front view).</p>
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<p>High-speed camera (HSC) set-up (<b>left</b>) and schematic illustration of the camera focal plane location in top view (<b>right</b>).</p>
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<p>Illustration of the plug flow model used to simulate gas–liquid mass transfer in the CLR.</p>
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<p>In-plane, time-averaged velocity contours of the fluid flow in the non-aerated CLR measured by PIV at 0.3 (<b>left</b>) and 1.4 m/s (<b>right</b>) inlet velocity.</p>
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<p>Contour of the time-averaged TKE measured by PIV at 1.4 m/s stream velocity.</p>
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<p>Aeration flow rate (<b>left</b>) and pipe cross-sectional void fraction (<b>right</b>) induced by the liquid stream at different water volumetric rates.</p>
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<p>Pressure drop of the non-aerated and aerated water flow in the CLR.</p>
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<p>HSC images of the aerated water streams in the CLR at different flow velocities.</p>
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<p>HSC images of the aerated water stream in the CLR along the flow at 1.1 m/s.</p>
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<p>Mean bubble size in the aerated water flow at different stream flow rates. The circle marks denote the results from PIV, while the rhomboids are from HSC.</p>
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<p>Oxygen transfer rates in the aerated water flow at different stream velocities in the CLR. <math display="inline"><semantics> <mrow> <mi>t</mi> <mn>95</mn> </mrow> </semantics></math> denotes the saturation time when <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>L</mi> </msub> <mo>/</mo> <msubsup> <mi>c</mi> <mi>L</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> reaches 95%.</p>
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<p>Comparison of the aeration efficiency achieved in the CLR with that of alternative spargers in various reactor configurations: closed-loop reactor (CLR), draft tube reactor (DTR), stirred tank reactor (STR), air-lift reactor (ALR), bubble column (BC) [<a href="#B34-processes-08-01498" class="html-bibr">34</a>,<a href="#B35-processes-08-01498" class="html-bibr">35</a>,<a href="#B36-processes-08-01498" class="html-bibr">36</a>,<a href="#B37-processes-08-01498" class="html-bibr">37</a>].</p>
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<p>Specific power requirement for water aeration of various sparger types (adopted from [<a href="#B38-processes-08-01498" class="html-bibr">38</a>]).</p>
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19 pages, 1017 KiB  
Article
Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process
by Titus Quah, Derek Machalek and Kody M. Powell
Processes 2020, 8(11), 1497; https://doi.org/10.3390/pr8111497 - 19 Nov 2020
Cited by 12 | Viewed by 4021
Abstract
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO [...] Read more.
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency. Full article
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<p>Markov decision process.</p>
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<p>Algorithm flow chart for offline preparation and online operation of a steady state system (<math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>).</p>
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<p>CSTR model.</p>
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<p>Methods’ learning curves on continuously stirred tank reactor (CSTR) optimization.</p>
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<p>Parity plots (Row 1) and residual plots (Row 2) for ANN-PSO and Proximal Policy Optimization (PPO) value functions. (<b>a</b>) ANN-PSO action-value function; (<b>b</b>) PPO value function.</p>
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<p>Online average computational times for ANN-PSO, PPO, and FP-NLP.</p>
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<p>Contour plots for methods’ profits, flows of B, and temperature set points over prices of A and q.</p>
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17 pages, 7206 KiB  
Article
Effect of Clearance and Cavity Geometries on Leakage Performance of a Stepped Labyrinth Seal
by Min Seok Hur, Soo In Lee, Seong Won Moon, Tong Seop Kim, Jae Su Kwak, Dong Hyun Kim and Il Young Jung
Processes 2020, 8(11), 1496; https://doi.org/10.3390/pr8111496 - 19 Nov 2020
Cited by 15 | Viewed by 5058
Abstract
This study evaluated the leakage characteristics of a stepped labyrinth seal. Experiments and computational fluid dynamics (CFD) analysis were conducted for a wide range of pressure ratios and clearance sizes, and the effect of the clearance on the leakage characteristics was analyzed by [...] Read more.
This study evaluated the leakage characteristics of a stepped labyrinth seal. Experiments and computational fluid dynamics (CFD) analysis were conducted for a wide range of pressure ratios and clearance sizes, and the effect of the clearance on the leakage characteristics was analyzed by determining the performance of the seal using a dimensionless parameter. It was observed from the analysis that the performance parameter of the seal decreases as the clearance size increases, but it tends to increase when the clearance size exceeds a certain value. In other words, it was revealed that there exists a specific clearance size (Smin) which minimizes the performance parameter of the seal. To identify the cause of this tendency change, a flow analysis was conducted using CFD. It was confirmed that the leakage characteristics of the stepped seal are affected by the size of the cavity, which is the space between the teeth. Therefore, a parametric study was conducted on the design parameters related to the cavity size (tooth height and pitch). The results show that the performance parameter decreases as the tooth height and pitch decreases. Moreover, Smin increases as the tooth height increases and the pitch decreases. Full article
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<p>Schematic diagram of the test facility.</p>
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<p>Seal geometry and parameters.</p>
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<p>Example of the computational domain and meshes (S/H = 0.4).</p>
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<p>Example of grid dependence of the computational fluid dynamics (CFD) result (S/H = 0.4).</p>
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<p>Comparison of turbulence models (S/H = 1.0, K/H = 4, D/H = 4).</p>
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<p>Comparison between the CFD and experimental results (K/H = 4, D/H = 4).</p>
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<p>Variations in flow function with pressure ratio (PR) and S/H (K/H = 4, D/H = 4).</p>
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<p>Variation in flow function with S/H (PR = 2.5, K/H = 4, D/H = 4).</p>
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<p>Total pressure contour plots for various S/H ratios (PR = 2.5, K/H = 4, D/H = 4).</p>
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<p>Static pressure contour plots for various S/H ratios (PR = 2.5, K/H = 4, D/H = 4).</p>
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<p>Variation in the averaged total pressure inside the cavity with S/H (PR = 2.5, K/H = 4, D/H = 4).</p>
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<p>Variation in the flow function with S/H and K/H (PR = 2.5, D/H = 4).</p>
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<p>Velocity vectors for various K/H ratios (PR = 2.5, S/H = 0.6, D/H = 4).</p>
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<p>Static pressure contour plots for the smallest and largest K/H ratios (PR = 2.5, S/H = 0.6, D/H = 4).</p>
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<p>Velocity contour plots for the smallest and largest K/H ratios (PR = 2.5, S/H = 0.6, D/H = 4).</p>
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<p>Variation in the flow function with S/H and D/H (PR = 2.5, K/H = 4).</p>
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<p>Velocity streamline plots for the smallest and largest D/H ratios (PR = 2.5, S/H = 0.6, K/H = 4).</p>
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<p>Velocity contour plots for the smallest and largest D/H ratios (PR = 2.5, S/H = 0.6, K/H = 4).</p>
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42 pages, 15891 KiB  
Article
Process Drive Sizing Methodology and Multi-Level Modeling Linking MATLAB® and Aspen Plus® Environment
by Patrik Furda, Miroslav Variny, Zuzana Labovská and Tomáš Cibulka
Processes 2020, 8(11), 1495; https://doi.org/10.3390/pr8111495 - 19 Nov 2020
Cited by 7 | Viewed by 4982
Abstract
Optimal steam process drive sizing is crucial for efficient and sustainable operation of energy-intense industries. Recent years have brought several methods assessing this problem, which differ in complexity and user-friendliness. In this paper, a novel complex method was developed and presented and its [...] Read more.
Optimal steam process drive sizing is crucial for efficient and sustainable operation of energy-intense industries. Recent years have brought several methods assessing this problem, which differ in complexity and user-friendliness. In this paper, a novel complex method was developed and presented and its superiority over other approaches was documented on an industrial case study. Both the process-side and steam-side characteristics were analyzed to obtain correct model input data: Driven equipment performance and efficiency maps were considered, off-design and seasonal operation was studied, and steam network topology was included. Operational data processing and sizing calculations were performed in a linked MATLAB®–Aspen Plus® environment, exploiting the strong sides of both software tools. The case study aimed to replace a condensing steam turbine by a backpressure one, revealing that: 1. Simpler methods neglecting frictional pressure losses and off-design turbine operation efficiency loss undersized the drive and led to unacceptable loss of deliverable power to the process; 2. the associated process production loss amounted up to 20%; 3. existing bottlenecks in refinery steam pipelines operation were removed; however, new ones were created; and 4. the effect on the marginal steam source operation may vary seasonally. These findings accentuate the value and viability of the presented method. Full article
(This article belongs to the Special Issue Chemical Process Design, Simulation and Optimization)
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Graphical abstract

Graphical abstract
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<p>Process scheme. Legend: BL = battery limit, cond. = condenser, CW = cooling water, C3A = propane, C3E = propylene, frac. = fraction, HPS = high-pressure steam, PP = polypropylene, prod. = production.</p>
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<p>Simplified plant steam network. Legend: Red line = HPS pipelines, orange line = middle-pressure steam (MPS) pipelines, dotted red line = turbine exhaust; blue line = condensate.</p>
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<p>Example of Aspen Plus<sup>®</sup>–MATLAB<sup>®</sup> link utilization (for details see <a href="#app1-processes-08-01495" class="html-app">Appendix A</a>).</p>
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<p>Seasonal fluctuations in low-pressure steam (LPS) export.</p>
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<p>Simplified plant steam network alteration proposal. Legend: Red line = HPS pipelines; orange line = MPS pipelines.</p>
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<p>Condensate to steam mass flow comparison.</p>
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<p>Measured to calculated shaft speed comparison.</p>
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<p>Comparison of measured and estimated condensate mass flow rate over the evaluated period.</p>
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<p>Measured to estimated condensate mass flow rate comparison.</p>
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<p>Process side characteristics.</p>
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<p>Effect of steam side parameters on isentropic efficiency.</p>
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<p>Relative isentropic efficiency as a function of relative shaft speed.</p>
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<p>HPS temperature histogram.</p>
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<p>HPS pressure histogram.</p>
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<p>MPS temperature histogram.</p>
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<p>MPS pressure histogram.</p>
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<p>Heat and pressure losses in steam network. Legend: Solid line = conditions at the CHP; dashed line = conditions at the fluid catalytic cracking (FCC) battery limit.</p>
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<p>Illustrative example of turbine characteristics.</p>
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<p>Isentropic efficiency as a function of power output.</p>
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<p>HPS transport velocity as a function of pipe diameter.</p>
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<p>Pipeline inner diameter. Legend: Solid line = existing pipeline; dashed line = new pipelines.</p>
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<p>HPS consumption over the evaluated period.</p>
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<p>Cumulative difference in HPS consumption (base case 1).</p>
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<p>Cumulative difference in HPS consumption (base case 7).</p>
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<p>Required to maximal power output differences for case 1 (<b>A</b>) and cases 4–6 (<b>B</b>) considering 100% pipeline length. Dashed line represents a typical 5% design reserve.</p>
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<p>Required to maximal power output difference for case 7 (<b>A</b>), case 9 (<b>B</b>) and cases 4–6 (<b>C</b>) considering 1000% pipeline length. Dashed line represents a typical 5% design reserve.</p>
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<p>Loss in production for a system considering 1000% pipeline length.</p>
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<p>Impact of proposed steam drive change on the HPS network. Long-term operation of the HPS network outside the &lt;0; 80&gt; t/h HPS export interval is infeasible.</p>
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<p>Impact of proposed steam drive change on the MPS network. Long-term operation of the MPS network below 20 t/h of exported MPS is infeasible.</p>
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<p>Economic evaluation.</p>
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<p>Process-side calculation (part 1).</p>
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<p>Process-side calculation (part 2).</p>
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<p>Process-side calculation (part 3).</p>
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<p>Process-side calculation (part 4).</p>
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<p>Steam-side calculation (part 1).</p>
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<p>Steam-side calculation (part 2).</p>
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<p>Steam-side calculation (part 3).</p>
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22 pages, 6724 KiB  
Article
How to Power the Energy–Water Nexus: Coupling Desalination and Hydrogen Energy Storage in Mini-Grids with Reversible Solid Oxide Cells
by Arianna Baldinelli, Linda Barelli, Gianni Bidini, Giovanni Cinti, Alessandro Di Michele and Francesco Mondi
Processes 2020, 8(11), 1494; https://doi.org/10.3390/pr8111494 - 19 Nov 2020
Cited by 17 | Viewed by 4007
Abstract
Sustainable Development Goals establish the main challenges humankind is called to tackle to assure equal comfort of living worldwide. Among these, the access to affordable renewable energy and clean water are overriding, especially in the context of developing economies. Reversible Solid Oxide Cells [...] Read more.
Sustainable Development Goals establish the main challenges humankind is called to tackle to assure equal comfort of living worldwide. Among these, the access to affordable renewable energy and clean water are overriding, especially in the context of developing economies. Reversible Solid Oxide Cells (rSOC) are a pivotal technology for their sector-coupling potential. This paper aims at studying the implementation of such a technology in new concept PV-hybrid energy storage mini-grids with close access to seawater. In such assets, rSOCs have a double useful effect: charge/discharge of the bulk energy storage combined with seawater desalination. Based on the outcomes of an experimental proof-of-concept on a single cell operated with salty water, the operation of the novel mini-grid is simulated throughout a solar year. Simulation results identify the fittest mini-grid configuration in order to achieve energy and environmental optimization, hence scoring a renewable penetration of more than 95%, marginal CO2 emissions (13 g/kWh), and almost complete coverage of load demand. Sector-coupling co-production rate (desalinated water versus electricity issued from the rSOC) is 0.29 L/kWh. Full article
(This article belongs to the Special Issue Recent Advances of Solid Oxide Fuel Cells (SOFC))
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Figure 1

Figure 1
<p>rSOC operation scheme: (<b>a</b>) Electrolysis (SOE), (<b>b</b>) Fuel cell (SOFC).</p>
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<p>rSOC test apparatus: cell housing.</p>
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<p>Polarization characterizations in SOFC and SOE mode, 800 °C.</p>
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<p>SOE endurance test, 800 °C, 250 mA/cm<sup>2</sup>, 50%–50% H2-H2O.</p>
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<p>EDX results of elemental mapping on the cell surface.</p>
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<p>Hybrid Energy Storage System: schematic architecture for stand-alone mini-grids.</p>
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<p>Management logic-tree: (<b>a</b>) Case S (RES Surplus), (<b>b</b>) Case L (Lack of RES).</p>
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<p>Sensitivity analysis of the key performance indicator <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> scored for the best mini-grid configuration reaching energy optimization. <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> contour maps are plotted vs: (<b>a</b>) σ<sub>PV</sub> and Π<sub>FW</sub>, for constant value of <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <msub> <mi>E</mi> <mrow> <mi>H</mi> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and Δ<sub>SOE</sub>; (<b>b</b>) versus <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <msub> <mi>E</mi> <mrow> <mi>H</mi> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and Δ<sub>SOE,</sub> for constant value of σ<sub>PV</sub> and Π<sub>FW</sub>.</p>
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<p>Sensitivity analysis of the key performance indicator <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>C</mi> <mi>O</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> scored in the best mini-grid configuration reaching environmental optimization. <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>C</mi> <mi>O</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> contour maps are plotted vs: (<b>a</b>) σ<sub>PV</sub> and Π<sub>FW</sub>, for constant value of <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <msub> <mi>E</mi> <mrow> <mi>H</mi> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and Δ<sub>SOE</sub>; (<b>b</b>) versus <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <msub> <mi>E</mi> <mrow> <mi>H</mi> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and Δ<sub>SOE,</sub> for constant value of σ<sub>PV</sub> and Π<sub>FW</sub>.</p>
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<p>Annual desalinated water production per-capita. Sensitivity analysis about: (<b>a</b>) σ<sub>PV</sub> and Π<sub>FW</sub>, for constant value of <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <msub> <mi>E</mi> <mrow> <mi>H</mi> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and Δ<sub>SOE</sub>; (<b>b</b>) versus <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <msub> <mi>E</mi> <mrow> <mi>H</mi> <mn>2</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> and Δ<sub>SOE,</sub> for constant value of σ<sub>PV</sub> and Π<sub>FW</sub>.</p>
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<p>A preliminary study on the variation of LCOE (€/kWh). Iso-cost plots are drawn against the PV surplus factor σ<sub>PV</sub> and the flywheel power ratio Π<sub>FW</sub>.</p>
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